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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
It is shown that, in comparison to other recently introduced large-scale datasets, TriviaQA has relatively complex, compositional questions, has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and requires more cross sentence reasoning to find answers.
QuAC: Question Answering in Context
QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation.
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
- Hannah Rashkin, Eunsol Choi, J. Jang, Svitlana Volkova, Yejin Choi
- Computer ScienceEMNLP
- 1 September 2017
Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
Zero-Shot Relation Extraction via Reading Comprehension
It is shown that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels.
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
- J. Clark, Eunsol Choi, Jennimaria Palomaki
- Linguistics, Computer ScienceTransactions of the Association for Computational…
- 10 March 2020
A quantitative analysis of the data quality and example-level qualitative linguistic analyses of observed language phenomena that would not be found in English-only corpora are presented.
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
- Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen
- Computer ScienceEMNLP
- 22 October 2019
In this task, 18 distinct question answering datasets were adapted and unified into the same format and the best system achieved an average F1 score of 72.5 on the 12 held-out datasets.
Ultra-Fine Entity Typing
A model that can predict ultra-fine types is presented, and is trained using a multitask objective that pools the authors' new head-word supervision with prior supervision from entity linking, and achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for newly-introduced datasets.
Scaling Semantic Parsers with On-the-Fly Ontology Matching
A new semantic parsing approach that learns to resolve ontological mismatches, which is learned from question-answer pairs, uses a probabilistic CCG to build linguistically motivated logicalform meaning representations, and includes an ontology matching model that adapts the output logical forms for each target ontology.
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
Neural Metaphor Detection in Context
These end-to-end neural models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.