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Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents
Catherine Chen, Zejiang Shen, Dan Klein, Gabi Stanovsky, Doug Downey, Kyle LoFindings of ACL • 2023 Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are often evaluated on documents with familiar layout features (e.g., papers from the…From Centralized to Ad-Hoc Knowledge Base Construction for Hypotheses Generation.
Shaked Launer-Wachs, Hillel Taub-Tabib, Jennie Tokarev Madem, Orr Bar-Natan, Yoav Goldberg, Y. ShamayJournal of Biomedical Informatics • 2023 Objective To demonstrate and develop an approach enabling individual researchers or small teams to create their own ad-hoc, lightweight knowledge bases tailored for specialized scientific interests, using text-mining over scientific literature, and…Answering Questions by Meta-Reasoning over Multiple Chains of Thought
Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, Jonathan BerantarXiv • 2023 Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through a voting mechanism…Lexical Generalization Improves with Larger Models and Longer Training
Elron Bandel, Yoav Goldberg, Yanai ElazarFinding of EMNLP • 2022 While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heuristics can lead to failure on challenging inputs. We analyze the use of lexical…Linear Adversarial Concept Erasure
Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan CotterellICML • 2022 We formulate the problem of identifying and erasing a linear subspace that corresponds to a given concept, in order to prevent linear predictors from recovering the concept. We model this problem as a constrained, linear minimax game, and show that existing…A Dataset for N-ary Relation Extraction of Drug Combinations
Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav GoldbergNAACL • 2022 Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available…Weakly Supervised Text-to-SQL Parsing through Question Decomposition
Tomer Wolfson, Daniel Deutch, Jonathan BerantFindings of NAACL • 2022 Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we…Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language
Royi Lachmy, Valentina Pyatkin, Reut TsarfatyACL • 2022 Forming and interpreting abstraction is a core process in human communication. In particular, when giving and performing complex instructions stated in natural language (NL), people may naturally evoke abstract constructs such as objects, loops, conditions…Large Scale Substitution-based Word Sense Induction
Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav GoldbergACL • 2022 We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where…Inferring Implicit Relations with Language Models
Uri Katz, Mor Geva, Jonathan BerantNAACL • UnImplicit • 2022 A prominent challenge for modern language understanding systems is the ability to answer implicit reasoning questions, where the required reasoning steps for answering the question are not mentioned in the text explicitly. In this work, we investigate why…