Commonsense Knowledge

Recognizing Mandarin ba sentences as the Ba Resultative Construction, this study implements a rigid test of Ding's (1993) treatment of ba as a matrix verb in the periphrastic resultative construction. …...

Feature norms can be regarded as repositories of common sense knowledge for basic level con- cepts. We acquire from very large corpora feature-norm-like concept descriptions using a combination of a …...

AbstractThere is by now widespread agreement that the most realistic way to construct the large-scale commonsense knowledge repositories required by natural language and artificial intelligence applications is by letting machines …...

Knowledge about how the world changes over time is a vital component of common- sense knowledge for Artificial Intelligence (AI) and natural language understanding. Actions and events are fundamental com- …...

We describe a novel approach for automat- ically predicting the hidden demographic properties of social media users. Building on prior work in common-sense knowl- edge acquisition from third-person text, we …...

Scripts representing common sense knowledge about stereotyped sequences of events have been shown to be a valu- able resource for NLP applications. We present a hierarchical Bayesian model for unsupervised …...

Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al. (2010)). Instead of inducing this knowledge in …...

Representation and learning of commonsense knowledge is one of the foundational prob- lems in the quest to enable deep language un- derstanding. This issue is particularly chal- lenging for understanding …...

AbstractOur ability to understand language often relies on common-sense knowledge ― background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in …...

Methods based on representation learning cur- rently hold the state-of-the-art in many natural language processing and knowledge base in- ference tasks. Yet, a major challenge is how to efficiently incorporate …...

In our paper we present our rule-based system for semantic processing. In particular we show examples and solutions that may be challenge our approach. We then discuss problems and shortcomings …...

AbstractCapabilities of detecting temporal and causal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the …...

AbstractCommonsense, real-world knowledge about the events that entities or “things in the world” are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw …...

In recent years, the importance of dialogue understanding systems has been increasing. However, it is difficult for computers to deeply understand our daily conversations because we frequently use emotional expressions …...

Commonsense knowledge plays an essential role in our language activities. Although many projects have aimed to develop language resources for commonsense knowledge, there is little work focusing on connotational meanings. …...

We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge. Our dataset …...

This paper proposes the task of Visual COPA (VCOPA). Given a premise image and two alternative images, the task is to identify the more plausible alternative with their commonsense causal …...

AbstractAutomatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay …...

AbstractPrior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event …...

AbstractWe introduce MCScript2.0, a machine comprehension corpus for the end-to-end evaluation of script knowledge. MCScript2.0 contains approx. 20,000 questions on approx. 3,500 texts, crowdsourced based on a new collection process …...

AbstractTo make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans …...

AbstractStory generation, namely, generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural …...

AbstractEmbedding commonsense knowledge is crucial for end-to-end models to generalize inference beyond training corpora. However, existing word analogy datasets have tended to be handcrafted, involving permutations of hundreds of words …...

AbstractRecently, large-scale pre-trained language models have demonstrated impressive performance on several commonsense-reasoning benchmark datasets. However, building machines with commonsense to compose realistically plausible sentences remains challenging. In this paper, we …...

AbstractIn this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such …...

AbstractMining commonsense knowledge from corpora suffers from reporting bias, over-representing the rare at the expense of the trivial (Gordon and Van Durme, 2013). We study to what extent pre-trained language …...

AbstractEmotion recognition in textual conversations (ERTC) plays an important role in a wide range of applications, such as opinion mining, recommender systems, and so on. ERTC, however, is a challenging …...

AbstractDespite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. …...

AbstractWe propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence …...

AbstractThe paper introduces two Russian machine reading comprehension (MRC) datasets, called MuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer. The former follows …...

AbstractIn this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC). For each of the questions, we invite annotators to first …...

In this paper, we consider a realistic scenario on stance detection with more application po- tential, i.e., zero-shot and few-shot stance de- tection, which identifies stances for a wide range …...

AbstractAbstractive dialogue summarization is the task of capturing the highlights of a dialogue andrewriting them into a concise version. In this paper we present a novel multi-speaker dialogue summarizer to …...

Detecting what emotions are expressed in text is a well-studied problem in natural lan- guage processing. However, research on finer- grained emotion analysis such as what causes an emotion is …...

Answering questions about why characters perform certain actions is central to under- standing and reasoning about narratives. De- spite recent progress in QA, it is not clear if existing models …...

AbstractLanguage models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand the …...

AbstractGrounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-reference is critical for dialogue comprehension and generation. In this paper, we …...

AbstractThe stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, …...

AbstractIn this paper, we study the effect of commonsense and domain knowledge while generating responses in counseling conversations using retrieval and generative methods for knowledge integration. We propose a pipeline …...

AbstractIn this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We present SICK, a framework that …...

AbstractEndowing the protagonist with a specific personality is essential for writing an engaging story. In this paper, we aim to control the protagonist’s persona in story generation, i.e., generating a …...

AbstractReasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the …...

AbstractPatronizing and Condescending Language (PCL) is a subtle but harmful type of discourse, yet the task of recognizing PCL remains under-studied by the NLP community. Recognizing PCL is challenging because …...

AbstractIn knowledge-grounded dialogue generation, pre-trained language models (PLMs) can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding. Unlike adopting …...

AbstractA commonsense knowledge resource organizes common sense that is not necessarily correct all the time, but most people are expected to know or believe. Such knowledge resources have recently been …...

Abstract“Zero-shot stance detection intends to detect previously unseen targets’ stances in the testingphase. However, achieving this goal can be difficult, as it requires minimizing the domain trans-fer between different targets, …...

AbstractArguments often do not make explicit how a conclusion follows from its premises. To compensate for this lack, we enrich arguments with structured background knowledge to support knowledge-intense argumentation tasks. …...