Generating Pattern-Based Entailment Graphs for Relation Extraction

Eichler, Kathrin; Xu, Fang; Uszkoreit, Hans; Krause, Sebastian

doi:10.18653/v1/s17-1026

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Future work might also look at entailment relation learning and link prediction tasks jointly. The entailment graphs can be used to improve relation extraction, similar to Eichler et al (2017), but covering more relations. In addition, we intend to collapse cliques in the entailment graphs to paraphrase clusters with a single relation identifier, to replace the form-dependent lexical semantics of the CCG parser with these form-independent relations (Lewis and Steedman, 2013a), and to use the entailment graphs to derive meaning postulates for use in tasks such as question-answering and construction of knowledge-graphs from text (Lewis and Steedman, 2014).…”

Section: Discussionmentioning

confidence: 99%

Learning Typed Entailment Graphs with Global Soft Constraints

Hosseini

Chambers

Reddy

et al. 2018

TACL

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This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.

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Section: Discussionmentioning

confidence: 99%

Learning Typed Entailment Graphs with Global Soft Constraints

Hosseini

Chambers

Reddy

et al. 2018

TACL

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show abstract

“…We follow the example of White et al (2017) and Poliak et al (2018) who reframe different NLP tasks, including extraction tasks, as NLI problems. Eichler et al (2017) and Obamuyide and Vlachos (2018) have both used NLI approaches for relation extraction. Our work differs in the information extracted and consequently in what context and hypothesis information we model.…”

Section: Related Workmentioning

confidence: 99%

Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

Hou

Jochim

Gleize

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.

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“…This system provides a textual entailment engine based on the edit distance between premise and hypothesis. This system was used as a feature for a semantic similarity task [46] and a relation extraction task [15]. The system was easy to be utilized, because it reveals token alignment information and does not require additional training data for the downstream task.…”

Section: Natural Language Inferencementioning

confidence: 99%

Explaining Text Matching on Neural Natural Language Inference

Kim

Jang

Allan

2020

ACM Trans. Inf. Syst.

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Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose lack of interpretability prohibits both straightforward integration and diagnosis for further improvement. We target the task of generating token-level explanations for NLI from a neural model. Many existing approaches for token-level explanation are either computationally costly or require additional annotations for training. In this article, we first introduce a novel method for training an explanation generator that does not require additional human labels. Instead, the explanation generator is trained with the objective of predicting how the model's classification output will change when parts of the inputs are modified. Second, we propose to build an explanation generator in a multi-task learning setting along with the original NLI task so the explanation generator can utilize the model's internal behavior. The experiment results suggest that the proposed explanation generator outperforms numerous strong baselines. In addition, our method does not require excessive additional computation at prediction time, which renders it an order of magnitude faster than the best-performing baseline.

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Generating Pattern-Based Entailment Graphs for Relation Extraction

Cited by 5 publications

References 20 publications

Learning Typed Entailment Graphs with Global Soft Constraints

Learning Typed Entailment Graphs with Global Soft Constraints

Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

Explaining Text Matching on Neural Natural Language Inference

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