TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification

August 10, 2018 · Entered Twilight · 🏛 Conference on Empirical Methods in Natural Language Processing

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Repo contents: cis, common_functions.py, demo_index.html, demo_preprocess.py, demo_server.py, demo_study_old_code.py, demo_test_function.py, fever_scorer.py, forfun.py, jsonlines.py, load_data.py, logistic_sgd.py, mlp.py, preprocess.py, test_FEVER_jointly_fine_fine.py, test_FEVER_jointly_fine_fine_#sent.py, train_FEVER.py, train_FEVER_error_analysis.py, train_FEVER_jointly_coarse_coarse_Context.py, train_FEVER_jointly_coarse_coarse_noContext.py, train_FEVER_jointly_coarse_coarse_noContext_noSharePara.py, train_FEVER_jointly_coarse_fine_maxsum.py, train_FEVER_jointly_coarse_fine_twocontext.py, train_FEVER_jointly_fine_coarse_noContext.py, train_FEVER_jointly_fine_fine_sentWiseEnsemble.py, train_FEVER_jointly_fine_fine_storemodel.py, train_FEVER_jointly_fine_fine_test_on_SciTail.py, train_FEVER_jointly_fine_fine_twocontext.py, word2embeddings

Authors Wenpeng Yin, Dan Roth arXiv ID 1808.03465 Category cs.CL: Computation & Language Citations 76 Venue Conference on Empirical Methods in Natural Language Processing Repository https://github.com/yinwenpeng/FEVER ⭐ 3 Last Checked 1 month ago

Abstract

Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment. However, given a large collection of text, finding evidence that could support or refute a given claim is a challenge in itself, amplified by the fact that different evidence might be needed to support or refute a claim. Nevertheless, most prior work decouples evidence identification from determining the truth value of the claim given the evidence. We propose to consider these two aspects jointly. We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence. Given the claim, TwoWingOS attempts to identify a subset of the evidence candidates; given the predicted evidence, it then attempts to determine the truth value of the corresponding claim. We treat this challenge as coupled optimization problems, training a joint model for it. TwoWingOS offers two advantages: (i) Unlike pipeline systems, it facilitates flexible-size evidence set, and (ii) Joint training improves both the claim entailment and the evidence identification. Experiments on a benchmark dataset show state-of-the-art performance. Code: https://github.com/yinwenpeng/FEVER