161 citations · 656 across the 43 of their papers we have counts for
16 papers · 2 filters
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension
Dheeru Dua, Ananth Gottumukkala, Alon Talmor +2
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 intr…
Neural Module Networks for Reasoning over Text
Nitish Gupta, Kevin Lin, Dan Roth +2
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module ne…
Question Answering is a Format; When is it Useful?
Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi +2
Recent years have seen a dramatic expansion of tasks and datasets posed as question answering, from reading comprehension, semantic role labeling, and even machine translation, to…
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Eric Wallace, Jens Tuyls, Junlin Wang +3
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation m…
Do NLP Models Know Numbers? Probing Numeracy in Embeddings
Eric Wallace, Yizhong Wang, Sujian Li +2
The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks. Currently, most NLP models treat numbers in text in the same way as other t…
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions
Oyvind Tafjord, Matt Gardner, Kevin Lin +1
We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., "A sunscreen…
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning
Pradeep Dasigi, Nelson F. Liu, Ana Marasović +2
Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex co…
Global Reasoning over Database Structures for Text-to-SQL Parsing
Ben Bogin, Matt Gardner, Jonathan Berant
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-…
Universal Adversarial Triggers for Attacking and Analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal +2
Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens…
Reasoning Over Paragraph Effects in Situations
Kevin Lin, Oyvind Tafjord, Peter Clark +1
A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind…
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling
Robert L. Logan, Nelson F. Liu, Matthew E. Peters +2
Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of rememberin…
Compositional Questions Do Not Necessitate Multi-hop Reasoning
Sewon Min, Eric Wallace, Sameer Singh +3
Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct l…
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
Ben Bogin, Matt Gardner, Jonathan Berant
Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both trainin…
Grammar-based Neural Text-to-SQL Generation
Kevin Lin, Ben Bogin, Mark Neumann +2
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar.…
Linguistic Knowledge and Transferability of Contextual Representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov +2
Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferabl…
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi +3
Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the br…