Обучение с подкреплением

Developing dialogue systems is a complex pro- cess. In particular, designing efficient dialogue management strategies is often difficult as there are no precise guidelines to develop them and no sure …...

We describe a hybrid approach to improv- ing search performance by providing a natural language front end to a traditional keyword-based search engine. The key component of the system is …...

Recent work in designing spoken dialogue systems has focused on using Reinforce- ment Learning to automatically learn the best action for a system to take at any point in the …...

We explore the use of restricted dialogue contexts in reinforcement learning (RL) of effective dialogue strategies for infor- mation seeking spoken dialogue systems (e.g. COMMUNICATOR (Walker et al., 2001)). The …...

Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of au- tomatically learning the best …...

Reinforcement learning gives a way to learn under what circumstances to per- form which actions. However, this ap- proach lacks a formal framework for spec- ifying hand-crafted restrictions, for speci- …...

Past approaches for using reinforcement learning to derive dialog control policies have assumed that there was enough col- lected data to derive a reliable policy. In this paper we present …...

We address two problems in the field of au- tomatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating the result with …...

We present a data-driven approach to learn user-adaptive referring expression gener- ation (REG) policies for spoken dialogue systems. Referring expressions can be dif- ficult to understand in technical domains where …...

We present a system that learns to fol- low navigational natural language direc- tions. Where traditional models learn from linguistic annotation or word distri- butions, our approach is grounded in …...

Cost-sensitive classification, where the features used in machine learning tasks have a cost, has been explored as a means of balancing knowl- edge against the expense of incrementally ob- taining …...

AbstractThe number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers. A DS …...

Optimization of a tutoring system from a fixed set of data In this paper, we present a general method for optimizing a tutoring system with a target application in the …...

We present a novel approach for automatic report generation from time-series data, in the context of student feedback genera- tion. Our proposed methodology treats content selection as a multi-label (ML) …...

AbstractThis paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734 dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). …...

We introduce a reinforcement learning- based approach to simultaneous ma- chine translation—producing a trans- lation while receiving input words— between languages with drastically dif- ferent word orders: from verb-final lan- …...

This paper explores alternate algorithms, reward functions and feature sets for per- forming multi-document summarization using reinforcement learning with a high focus on reproducibility. We show that ROUGE results can …...

This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multi- domain Spoken Dialogue System built on a distributed architecture. In the proposed …...

Coreference resolution systems are typically trained with heuristic loss functions that re- quire careful tuning. In this paper we in- stead apply reinforcement learning to directly optimize a neural mention-ranking …...

Most successful information extraction sys- tems operate with access to a large collec- tion of documents. In this work, we explore the task of acquiring and incorporating exter- nal evidence …...

We describe a question answering model that applies to both images and structured knowl- edge bases. The model uses natural lan- guage strings to automatically assemble neu- ral networks from …...

When humans read text, they fixate some words and skip others. However, there have been few attempts to explain skipping behav- ior with computational models, as most ex- isting work …...

AbstractHand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable …...

AbstractWe investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory …...

AbstractWe present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension …...

AbstractWe study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use …...

AbstractNatural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of …...

AbstractTo enable human-robot communication and collaboration, previous works represent grounded verb semantics as the potential change of state to the physical world caused by these verbs. Grounded verb semantics are …...

AbstractRecent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling …...

AbstractWe propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of …...

AbstractThis paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work …...

AbstractWe study the problem of semi-supervised question answering—utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this …...

AbstractI explore the hypothesis that conventional neural network models (e.g., recurrent neural networks) are incorrectly biased for making linguistically sensible generalizations when learning, and that a better class of models …...

AbstractError propagation is a common problem in NLP. Reinforcement learning explores erroneous states during training and can therefore be more robust when mistakes are made early in a process. In …...

AbstractSearch engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far …...

Supervised approaches for text summari- sation suffer from the problem of mis- match between the target labels/scores of individual sentences and the evaluation score of the final summary. Reinforcement learning …...

AbstractVisual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from …...

AbstractThere has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a …...

AbstractIn this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues include applying reinforcement learning with user …...

AbstractIn this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text. In particular, we propose to learn neural rewards …...

AbstractTarget-dependent classification tasks, such as aspect-level sentiment analysis, perform fine-grained classifications towards specific targets. Semantic compositions over tree structures are promising for such tasks, as they can potentially capture long-distance …...

AbstractThe named concepts and compositional operators present in natural language provide a rich source of information about the abstractions humans use to navigate the world. Can this linguistic background knowledge …...

AbstractSequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no …...

AbstractThe interpretation of spatial references is highly contextual, requiring joint inference over both language and the environment. We consider the task of spatial reasoning in a simulated environment, where an …...

AbstractTo deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. An …...

AbstractGenerating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of tokens always suffers from …...

AbstractRecently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the learned trees often do not match actual syntax trees …...

AbstractGenerating intriguing question is a key step towards building human-like open-domain chatbots. Although some recent works have focused on this task, compared with questions raised by humans, significant gaps remain …...

AbstractSentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via …...

AbstractSentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article. In this paper, we show that recent …...

AbstractRumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; …...

AbstractRecent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence …...

AbstractWe consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, …...

AbstractThis paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills. …...

AbstractNegation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: (1) such granular annotations are …...

AbstractNatural language has recently been explored as a new medium of supervision for training machine learning models. Here, we explore learning classification tasks using language in a conversational setting – …...

AbstractSoft-attention based Neural Machine Translation (NMT) models have achieved promising results on several translation tasks. These models attend all the words in the source sequence for each target token, which …...

AbstractGenerating keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicting multiple keyphrases for an …...

AbstractIn recent years, advances in neural variational inference have achieved many successes in text processing. Examples include neural topic models which are typically built upon variational autoencoder (VAE) with an …...

AbstractText-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning …...

AbstractSupervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance. To solve the problem, training data selection (TDS) has …...

AbstractText style transfer rephrases a text from a source style (e.g., informal) to a target style (e.g., formal) while keeping its original meaning. Despite the success existing works have achieved …...

AbstractFor evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, …...

AbstractIn this paper, we alleviate the local optimality of back-translation by learning a policy (takes the form of an encoder-decoder and is defined by its parameters) with future rewarding under …...

AbstractThe ability to engage in goal-oriented conversations has allowed humans to gain knowledge, reduce uncertainty, and perform tasks more efficiently. Artificial agents, however, are still far behind humans in having …...

AbstractNeural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of these systems—fathoming how and when neural-based systems fail …...

AbstractThe challenge of both achieving task completion by querying the knowledge base and generating human-like responses for task-oriented dialogue systems is attracting increasing research attention. In this paper, we propose …...

AbstractDeep question generation (DQG) aims to generate complex questions through reasoning over multiple documents. The task is challenging and underexplored. Existing methods mainly focus on enhancing document representations, with little …...

AbstractWe propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of …...

AbstractWe show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this issue, we propose …...

AbstractIn many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the user had in mind. In this paper, we show how cognitive models of …...

AbstractReinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy. However, these methods suffer from sparse and unstable reward signals returned by a …...

AbstractWhile recent advances in language modeling has resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style. …...

AbstractQuestion generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of triples. Two main challenges still face the current crop of encoder-decoder-based methods, …...

AbstractOpen attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a question-answering (QA) task. While the collections of …...

AbstractWalk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during a …...

AbstractTraining a task-oriented dialogue agent with reinforcement learning is prohibitively expensive since it requires a large volume of interactions with users. Human demonstrations can be used to accelerate learning progress. …...

AbstractMost existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treats the opposite agent policy as part of the environment. …...

AbstractWe consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language. These problems are often tackled with reinforcement learning approaches. We find that these …...

AbstractA Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system. Tremendous progress has been made in recent years. However, the major challenges remain. The state-of-the-art …...

AbstractMany studies have applied reinforcement learning to train a dialog policy and show great promise these years. One common approach is to employ a user simulator to obtain a large …...

AbstractWhile neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances …...

AbstractReinforcement learning (RL) has made remarkable progress in neural machine translation (NMT). However, it exists the problems with uneven sampling distribution, sparse rewards and high variance in training phase. Therefore, …...

AbstractDeep neural network models have helped named entity recognition achieve amazing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Efforts have been made …...

AbstractWe propose a method to automatically generate a domain- and task-adaptive maskings of the given text for self-supervised pre-training, such that we can effectively adapt the language model to a …...

AbstractThe manual labeling work for constructing the Korean corpus is too time-consuming and laborious. It is difficult for low-minority languages to integrate resources. As a result, the research progress of …...

AbstractIn this paper, we study machine reading comprehension (MRC) on long texts: where a model takes as inputs a lengthy document and a query, extracts a text span from the …...

AbstractQuestion generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural …...

AbstractDialogue policy learning for task-oriented dialogue systems has enjoyed great progress recently mostly through employing reinforcement learning methods. However, these approaches have become very sophisticated. It is time to re-evaluate …...

AbstractLanguage-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction following and …...

AbstractData-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating …...

AbstractWe study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary …...

AbstractOne of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora. The diversity of open domain corpora and the variety …...

AbstractEvaluating the trustworthiness of a model’s prediction is essential for differentiating between ‘right for the right reasons’ and ‘right for the wrong reasons’. Identifying textual spans that determine the target …...

AbstractZero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen …...

AbstractPretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection …...

The advent of large pre-trained language mod- els has made it possible to make high-quality predictions on how to add or change a sentence in a document. However, the high …...

AbstractNeural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by …...

AbstractMulti-document question generation focuses on generating a question that covers the common aspect of multiple documents. Such a model is useful in generating clarifying options. However, a naive model trained …...

AbstractA mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and …...

AbstractThis paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation quality and low latency; …...

AbstractWe seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced …...

AbstractFor task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To …...

AbstractNeural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors. However, existing report generation …...

AbstractNeural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker’s persona. These models are trained with fully supervised learning …...

AbstractExposure bias and poor translation diversity are two common problems in neural machine trans-lation (NMT) which are caused by the general of the teacher forcing strategy for training inthe NMT …...

AbstractReinforcement learning has been proved to be effective in handling low resource machine trans-lation tasks and different sampling methods of reinforcement learning affect the performance ofthe model. The reward for …...

AbstractThe element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session. In contrast to a single text, a session may …...

AbstractRecently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious deficiencies. (1)They usually consider only …...

AbstractDeparting from both sequential pipelines and monotask systems, we propose Multiple Tasks Integration (MTI), a multitask paradigm orthogonal to weight sharing. The essence of MTI is to process the input …...

AbstractWhen intelligent agents communicate to accomplish shared goals, how do these goals shape the agents’ language? We study the dynamics of learning in latent language policies (LLPs), in which instructor …...

Defining a sophisticated action space for a dia- log agent is essential for efficient training with reinforcement learning (RL). Recent work in- troduces discrete latent variables to use as an …...

AbstractIt is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news …...

AbstractData augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed …...

Event extraction (EE) aims to harvest event in- stances from plain text, where each instance is composed of a group of event arguments with specific event roles. Existing end-to-end EE …...

Recently, unsupervised parsing of syntactic trees has gained considerable attention. prototypical approach to such unsupervisedA parsing employs reinforcement learning and auto-encoders. However, no mechanism en- sures that the learnt model …...

Multi-hop reasoning is an effective and ex- plainable approach to predicting missing facts in Knowledge Graphs (KGs). It usually adopts the Reinforcement Learning (RL) framework and searches over the KG …...

In reinforcement learning (RL) based task- oriented dialogue systems, users act as the en- vironment and the agent learns the policy by interacting with users. However, due to the subjectivity …...

AbstractTemporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. …...

AbstractMachine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e.g. sentiment and emotion and gender traits and etc.) to the target and …...

AbstractTemporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing research interest in recent years. Most existing methods focus on reasoning at past timestamps to complete the …...

AbstractExisting pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications. To address this issue, we propose a dynamic token reduction approach …...

AbstractOne of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue system and …...

AbstractTask-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog …...

AbstractReinforcement Learning has shown success in a number of complex virtual environments. However, many challenges still exist towards solving problems with natural language as a core component. Interactive Fiction Games …...

AbstractWith a handful of demonstration examples, large-scale language models demonstrate strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning …...

AbstractWhen a natural language generation (NLG) component is implemented in a real-world task-oriented dialogue system, it is necessary to generate not only natural utterances as learned on training data but …...

AbstractWe focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games—environments wherein an agent perceives and interacts with a world …...

AbstractSocratic questioning is an educational method that allows students to discover answers to complex problems by asking them a series of thoughtful questions. Generation of didactically sound questions is challenging, …...

AbstractComposing Ci (also widely known as Song Ci), a special type of classical Chinese poetry, requires to follow particular format once their tune patterns are given. To automatically generate a …...

AbstractGreedy algorithms for NLP such as transition-based parsing are prone to error propagation. One way to overcome this problem is to allow the algorithm to backtrack and explore an alternative …...

AbstractContinual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics and tasks, a task-oriented …...

AbstractSentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized …...

AbstractCurrent metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modality and thus are not adequate …...

AbstractSemantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates. Semantic arguments and their predicates are related in various distinct manners, of which certain semantic …...

AbstractParaphrase generation is an important language generation task attempting to interpret user intents and systematically generate new phrases of identical meanings to the given ones. However, the effectiveness of paraphrase …...

AbstractInvestigating cooperativity of interlocutors is central in studying pragmatics of dialogue. Models of conversation that only assume cooperative agents fail to explain the dynamics of strategic conversations. Thus, we investigate …...

AbstractWe introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspective revision …...

AbstractIncorporating personas information allows diverse and engaging responses in dialogue response generation. Unfortunately, prior works have primarily focused on self personas and have overlooked the value of partner personas. Moreover, …...

AbstractPersuasive conversations for a social cause often require influencing other person’s attitude or intention that may fail even with compelling arguments. The use of emotions and different types of polite …...

AbstractWorld models have improved the ability of reinforcement learning agents to operate in a sample efficient manner, by being trained to predict plausible changes in the underlying environment. As the …...

AbstractKnowledge underpins reasoning. Recent research demonstrates that when relevant knowledge is provided as additional context to commonsense question answering (QA), it can substantially enhance the performance even on top of …...

AbstractMedical images are widely used in clinical decision-making, where writing radiology reports is a potential application that can be enhanced by automatic solutions to alleviate physicians’ workload. In general, radiology …...

AbstractApplying Reinforcement learning (RL) following maximum likelihood estimation (MLE) pre-training is a versatile method for enhancing neural machine translation (NMT) performance. However, recent work has argued that the gains produced …...

AbstractNatural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. …...

AbstractClass imbalance naturally exists when label distributions are not aligned across source and target domains. However, existing state-of-the-art UDA models learn domain-invariant representations across domains and evaluate primarily on class-balanced …...

AbstractContext is everything, even in commonsense moral reasoning. Changing contexts can flip the moral judgment of an action; Lying to a friend is wrong in general, but may be morally …...

AbstractA modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is …...

AbstractDespite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like …...

AbstractReinforcement learning has been widely adopted to model dialogue managers in task-oriented dialogues. However, the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of human behaviour. The …...

AbstractPrompt-tuning based few-shot learning has garnered increasing attention in recent years due to its efficiency and promising capability. To achieve the best performance for NLP tasks with just a few …...

AbstractWe propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D&D), a role-playing game, provides an ideal setting to …...

AbstractMulti-hop reasoning, a prevalent approach for query answering, aims at inferring new facts along reasonable paths over a knowledge graph. Reinforcement learning methods can be adopted by formulating the problem …...

AbstractTo align conditional text generation model outputs with desired behaviors, there has been an increasing focus on training the model using reinforcement learning (RL) with reward functions learned from human …...

AbstractIn this work, we study reinforcement learning (RL) in solving text-based games. We address the challenge of combinatorial action space, by proposing a confidence-based self-imitation model to generate action candidates …...

Abstract“Relation Extraction (RE) task aims to discover the semantic relation that holds between two entitiesand contributes to many applications such as knowledge graph construction and completion. Reinforcement Learning (RL) has …...