Мәтіндік қорытынды

In this paper we define a novel similarity measure between examples of textual en- tailments and we use it as a kernel func- tion in Support Vector Machines (SVMs). This …...

Recognizing textual entailment is a chal- lenging problem and a fundamental com- ponent of many applications in natural language processing. We present a novel framework for recognizing textual entail- ment …...

Work on the semantics of questions has argued that the relation between a ques- tion and its answer(s) can be cast in terms of logical entailment. In this paper, we …...

The end-to-end performance of natural language processing systems for com- pound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline archi- …...

The majority of the state-of-the-art ap- proaches to recognizing textual entailment focus on defining a generic approach to RTE. A generic approach never works well for every single entailment pair: …...

Semantic inference is a key component for advanced natural language under- standing. However, existing collections of automatically acquired inference rules have shown disappointing results when used in applications such as …...

AbstractThe paper reports on completed work aimed at the creation of a resource, namely, the Greek Textual Entailment Corpus (GTEC) that is appropriate for guiding training and evaluation of a …...

This paper studies textual inference by inves- tigating comma structures, which are highly frequent elements whose major role in the ex- traction of semantic relations has not been hitherto recognized. …...

Most Web-based Q/A systems work by find- ing pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple …...

This paper addresses question analysis in the framework of Question Answering over struc- tured data. The problem is set as a relation extraction task, where all the relations of inter- …...

AbstractDiscourse phenomena play a major role in text processing tasks. However, so far relatively little study has been devoted to the relevance of discourse phenomena for inference. Therefore, an experimental …...

AbstractTextual entailment has been recognized as a generic task that captures major semantic inference needs across many natural language processing applications. However, to date, textual entailment has not been considered …...

AbstractThis paper focuses on the improvement of the conceptual structure of FrameNet (FN) for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, …...

This paper proposes a general learning frame- work for a class of problems that require learn- ing over latent intermediate representations. Many natural language processing (NLP) de- cision problems are …...

AbstractWe focus on textual entailments mediated by syntax and propose a new methodology to evaluate textual entailment recognition systems on such data. The main idea is to generate a syntactically …...

While a significant amount of research has been devoted to textual entailment, automated entailment from conversational scripts has re- ceived less attention. To address this limi- tation, this paper investigates …...

We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demon- strate that they offer a simple, intuitive, and effective method for modeling pairs …...

This paper presents a novel method for recognizing textual entailment which de- rives the hypothesis from the text through a sequence of parse tree transforma- tions. Unlike related approaches based …...

In this paper we report the results obtained in the Semantic Textual Similarity (STS) task, with a system primarily developed for textual entailment. Our results are quite promising, getting a …...

We propose a new shared task on grading stu- dent answers with the goal of enabling well- targeted and flexible feedback in a tutorial di- alogue setting. We provide an …...

We combine logical and distributional rep- resentations of natural language meaning by transforming distributional similarity judg- ments into weighted inference rules using Markov Logic Networks (MLNs). We show that this …...

The results obtained by the BUAP team at Task 1 of SemEval 2014 are presented in this paper. The run submitted is a supervised ver- sion based on two classification …...

This paper describes the two procedures for determining the semantic similarities between sentences submitted for the Se- mEval 2014 Task 1. MeanMaxSim, an unsupervised procedure, is proposed as a new …...

AbstractBeside formal approaches to semantic inference that rely on logical representation of meaning, the notion of Textual Entailment (TE) has been proposed as an applied framework to capture major semantic …...

This paper presents our approach to se- mantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two ques- tions: (1) Can we solve …...

Sentence Connectivity is a textual charac- teristic that may be incorporated intelli- gently for the selection of sentences of a well meaning summary. However, the ex- isting summarization methods do …...

Similarity between natural language texts, sentences in terms of meaning, known as tex- tual entailment, is a generic problem in the area of computational linguistics. In the last few years …...

A major problem in research on Textual Entail- ment (TE) is the high implementation effort for TE systems. Recently, interoperable standards for annotation and preprocessing have been proposed. In contrast, …...

AbstractHow to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work …...

AbstractSeveral tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification). For several single sequence …...

AbstractIn this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, …...

AbstractBar exams provide a key watershed by which legal professionals demonstrate their knowledge of the law and its application. Passing the bar entitles one to practice the law in a …...

The interpretation of adjective-noun pairs plays a crucial role in tasks such as rec- ognizing textual entailment. Formal se- mantics often places adjectives into a taxonomy which should dictate adjec- …...

AbstractDeep learning techniques are increasingly popular in the textual entailment task, overcoming the fragility of traditional discrete models with hard alignments and logics. In particular, the recently proposed attention models …...

AbstractWe approach the recognition of textual entailment using logical semantic representations and a theorem prover. In this setup, lexical divergences that preserve semantic entailment between the source and target texts …...

AbstractThis work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments …...

AbstractText similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance …...

AbstractWe introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across …...

AbstractIn this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical …...

We propose ESCRITO, a toolkit for scoring student writings using NLP techniques that addresses two main user groups: teachers and NLP researchers. Teachers can use a high-level API in the …...

AbstractCapturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise …...

Commonsense knowledge as provided by scripts is crucially relevant for text understanding systems, providing a basis for commonsense inference. This paper considers a relevant subtask of script-based text understanding, the …...

Computational argumentation aims to model arguments as a set of premises that either support each other or collectively support a conclusion. We prepare three datasets of text-hypothesis pairs with support-based …...

AbstractZero-shot text classification (0Shot-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0Shot-TC aims to associate an appropriate label with a piece …...

AbstractScenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a …...

AbstractWhen constructing models that learn from noisy labels produced by multiple annotators, it is important to accurately estimate the reliability of annotators. Annotators may provide labels of inconsistent quality due …...

AbstractApplications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based …...

AbstractIn today’s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a …...

AbstractIn this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead …...

AbstractIn this paper, we present a methodology for decomposing and comparing multiple meaning relations (paraphrasing, textual entailment, contradiction, and specificity). The methodology includes SHARel - a new typology that consists …...

AbstractDespite significant progress in text generation models, a serious limitation is their tendency to produce text that is factually inconsistent with information in the input. Recent work has studied whether …...

AbstractMany datasets have been shown to contain incidental correlations created by idiosyncrasies in the data collection process. For example, sentence entailment datasets can have spurious word-class correlations if nearly all …...

AbstractOne of the cardinal tasks in achieving robust medical question answering systems is textual entailment. The existing approaches make use of an ensemble of pre-trained language models or data augmentation, …...

AbstractIt is well known that the standard likelihood training and approximate decoding objectives in neural text generation models lead to less human-like responses for open-ended tasks such as language modeling …...

AbstractAn NLP model’s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource …...

AbstractA standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we …...

AbstractThe input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with …...

AbstractMany open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses. A QA system then determines if …...

AbstractWe explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of …...

Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since …...

AbstractMultilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability. Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned …...

AbstractThe quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural …...

AbstractWhile pretrained language models have exhibited impressive generalization capabilities, they still behave unpredictably under certain domain shifts. In particular, a model may learn a reasoning process on in-domain training data …...

AbstractWe describe our participation on the Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT) of SemEval’23. The organizers provided a collection of clinical trials as training data and a …...

AbstractIn this paper, we describe our system for SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. Given a CTR premise, and a statement, this task involves 2 …...