Incorporating Temporal Information in Entailment Graph Mining
We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose $\rightarrow$ play, while avoiding the pitfall of learning non-entailments such as win $\not\rightarrow$ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.
Code (1)
Tasks
Graph MiningSimilar Papers 제목 키워드 기반
Blindness to Modality Helps Entailment Graph Mining
Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also be expected to benefit from attention to …
Graph LearningGraph MiningQuestion AnsweringBMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs
In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based…
Lexical EntailmentSemantic ParsingTask 2Temporal and Aspectual Entailment
Inferences regarding "Jane's arrival in London" from predications such as "Jane is going to London" or "Jane has gone to London" depend on tense and aspect of the predications. Tense determines the temporal location of t…
Natural Language InferenceRepurposing Entailment for Multi-Hop Question Answering Tasks
Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with multiple sentences, …
Multi-hop Question AnsweringQuestion AnsweringSentenceInfusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approaches have shown that information from ex…
Knowledge GraphsNatural Language Inference