Papers by Daniel Andor
A Challenge Set and Methods for Noun-Verb Ambiguity (D18-1)
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| Challenge: | English part-of-speech taggers make egregious errors related to noun-verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since 2002. |
| Approach: | They propose to use a WSJ dataset to identify 30,000 examples of noun-verb ambiguity . they find that english part-of-speech taggers make egregious errors related to nouns and verbs . |
| Outcome: | The proposed model improves on the WSJ Penn Treebank by 14% and 52% relative to the previous model. |
Synthetic QA Corpora Generation with Roundtrip Consistency (P19-1)
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| Challenge: | Existing methods for generating synthetic question answering corpora are not suitable for QA, but can be constructed from widely available natural text. |
| Approach: | They propose a method for generating synthetic question answering corpora by combining question generation and answer extraction models and filtering the results to ensure roundtrip consistency. |
| Outcome: | The proposed model achieves exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2 and NQ. |
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (D19-1)
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| Challenge: | End-to-end reading comprehension models have been successful at extracting text answers, but there are still problems with generalizing them to abstractive numerical reasoning. |
| Approach: | They propose to augment a BERT-based reading comprehension model with a set of executable ‘programs’ which encompass simple arithmetic as well as extraction. |
| Outcome: | The proposed model can perform 33% absolute improvement on the DROP dataset, with very few training examples. |
QED: A Framework and Dataset for Explanations in Question Answering (2021.tacl-1)
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Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins
| Challenge: | Existing question answering systems provide no explanation of reasoning that leads to answer . linguistically informed, extensible framework provides explanations in question answering . |
| Approach: | They propose a linguistically informed, extensible framework for explanations in question answering . they propose an expert-annotated dataset of QED explanations built upon a subset of the Natural Questions dataset . |
| Outcome: | The proposed framework improves the ability of untrained raters to spot errors in QA datasets. |
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
Linguistically-Informed Self-Attention for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models of semantic role labeling use no explicit linguistic features. prior work has shown that syntax trees can dramatically improve SRL decoding. |
| Approach: | They propose a neural network model that incorporates syntax using only raw tokens . they show that LISA out-performs the state-of-the-art with contextually-encoded word representations a 1.0 F1 on newswire and 2.0 F1 in out-of domain text . |
| Outcome: | The proposed model outperforms the state-of-the-art model with word embeddings and predicted predicates. |