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Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
Ben Bogin, Sanjay Subramanian, Matt Gardner, Jonathan BerantTACL • 2020 Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-ofthe-art models in grounded question answering often do not explicitly perform decomposition…When Bert Forgets How To POS: Amnesic Probing of Linguistic Properties and MLM Predictions
Yanai Elazar, Shauli Ravfogel, Alon Jacovi, Yoav GoldbergTACL • 2020 A growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability…Evaluating NLP Models via Contrast Sets
M.Gardner, Y.Artzi, V.Basmova, J.Berant, B.Bogin, S.Chen, P.Dasigi, D.Dua, Y.Elazar, A.Gottumukkala, N.Gupta, H.Hajishirzi, G.Ilharco, D.Khashabi, K.Lin, J.Liu, N.Liu, P.Mulcaire, Q.Ning, S.Singh, N.Smith, S.Subramanian, R.Tsarfaty, E.Wallace, et.alarXiv • 2020 Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on…Differentiable Scene Graphs
Moshiko Raboh, Roei Herzig, Gal Chechik, Jonathan Berant, Amir Globerson WACV • 2020 Understanding the semantics of complex visual scenes involves perception of entities and reasoning about their relations. Scene graphs provide a natural representation for these tasks, by assigning labels to both entities (nodes) and relations (edges…On Making Reading Comprehension More Comprehensive
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon MinEMNLP • MRQA Workshop • 2019 Machine reading comprehension, the task of evaluating a machine’s ability to comprehend a passage of text, has seen a surge in popularity in recent years. There are many datasets that are targeted at reading comprehension, and many systems that perform as…ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension
Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt GardnerEMNLP • MRQA Workshop • 2019 Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple…Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing
Jonathan Herzig, Jonathan BerantEMNLP • 2019 A major hurdle on the road to conversational interfaces is the difficulty in collecting data that maps language utterances to logical forms. One prominent approach for data collection has been to automatically generate pseudo-language paired with logical…Global Reasoning over Database Structures for Text-to-SQL Parsing
Ben Bogin, Matt Gardner, Jonathan BerantEMNLP • 2019 State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database…Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training
Hila Gonen, Yoav GoldbergEMNLP • 2019 We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three reasons: (1) lack of available large-scale code…On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar and Jonathan BerantCoNLL • 2019One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively…Best Paper Honorable Mention