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We present a system for deciding whether a given sentence can be inferred from text. Each sentence is represented as a directed graph (extracted from a depen- dency parser) in …...

The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine transla- tion (MT). But the tools and …...

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We ex- plore the use of Amazon’s Mechanical Turk system, a significantly cheaper …...

In this paper, we introduce our Recognizing Textual Entailment (RTE) system developed on the basis of Lexical Entailment between two text excerpts, namely the hypothesis and the text. To extract …...

In this paper, we first compare several strategies to handle the newly proposed three-way Recognizing Textual Entailment (RTE) task. Then we define a new mea- surement for a pair of …...

We challenge the NLP community to par- ticipate in a large-scale, distributed effort to design and build resources for devel- oping and evaluating solutions to new and existing NLP tasks …...

AbstractMany natural language processing tasks, including information extraction, question answering and recognizing textual entailment, require analysis of the polarity, focus of polarity, tense, aspect, mood and source of the event …...

Determining whether two terms in text have an ancestor relation (e.g. Toyota and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in …...

AbstractThis paper focuses on the central role played by lexical information in the task of Recognizing Textual Entailment. In particular, the usefulness of lexical knowledge extracted from several widely used …...

A range of Natural Language Process- ing tasks involve making judgments about the semantic relatedness of a pair of sen- tences, such as Recognizing Textual En- tailment (RTE) and answer …...

In this paper, we describe an approach based on off-the-shelf parsers and semantic re- sources for the Recognizing Textual Entail- ment (RTE) challenge that can be generally applied to any …...

This paper presents CELI’s participation in the SemEval The Joint Student Response Anal- ysis and 8th Recognizing Textual Entailment Challenge (Task7) and Cross-lingual Textual Entailment for Content Synchronization task (Task …...

We present the results of the Joint Student Response Analysis and 8th Recognizing Tex- tual Entailment Challenge, aiming to bring to- gether researchers in educational NLP tech- nology and textual …...

AbstractFrom a purely theoretical point of view, it makes sense to approach recognizing textual entailment (RTE) with the help of logic. After all, entailment matters are all about logic. In …...

Shared Task 1 of SemEval-2014 com- prised two subtasks on the same dataset of sentence pairs: recognizing textual en- tailment and determining textual similar- ity. We used an existing system …...

Evaluation of NLP methods requires test- ing against a previously vetted gold-standard test set and reporting standard metrics (ac- curacy/precision/recall/F1). The current as- sumption is that all items in a …...

AbstractRecognizing Textual Entailment (RTE) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible …...

AbstractHumans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. …...

AbstractIn the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size …...

The quality of training data is one of the crucial problems when a learning-centered approach is employed. This paper proposes a new method to investigate the quality of a large …...

AbstractWhile neural networks produce state-of-the-art performance in many NLP tasks, they generally learn from lexical information, which may transfer poorly between domains. Here, we investigate the importance that a model …...

AbstractAdversarial domain adaptation has been recently proposed as an effective technique for textual matching tasks, such as question deduplication. Here we investigate the use of gradient reversal on adversarial domain …...

AbstractIn this paper, we propose a method to modify natural textual entailment problem datasets so that they better reflect a more precise notion of entailment. We apply this method to …...

AbstractNatural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), has been one of the central tasks in Artificial Intelligence (AI) and Natural Language Processing (NLP). RTE between the …...