--- title: "Mike Lewis — papers and cards" description: "Mike Lewis: 5 paper cards in the Research library (2014–2017), with what each paper is about, the ideas Mova took from it, and the co-authors." canonical: "https://research.elamur.ai/free/en/authors/mike-lewis/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/mike-lewis/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Mike Lewis # Mike Lewis 5 paper cards in the Research library · 2014–2017 Mike Lewis wrote 5 of the papers Mova reads, in 2014–2017, most of them on [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/), [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/); often with [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/), [Kenton Lee](https://md.research.elamur.ai/free/en/authors/kenton-lee/), [Luheng He](https://md.research.elamur.ai/free/en/authors/luheng-he/). [OpenAlex](https://openalex.org/A5004412943) · [ORCID](https://orcid.org/0000-0003-0679-6612) · [**h-index 38**](https://openalex.org/authors/A5004412943) · [36864 citations](https://openalex.org/authors/A5004412943) · [84 works](https://openalex.org/works?filter=author.id:A5004412943) — openalex-snapshot, 2026-10-14 The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Mike+Lewis) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Mike+Lewis) · [dblp](https://dblp.org/search?q=Mike+Lewis) · [ACL Anthology](https://aclanthology.org/search/?q=Mike+Lewis) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Mike+Lewis) Topics: [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) 2 · [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) 1 ## Papers 1. **[End-to-end Neural Coreference Resolution](https://md.research.elamur.ai/free/en/papers/lee-2017-end-end-neural-coreference-resolution/)** *(2017)* The first coreference model trained end to end without a parser or a mention detector. Every span up to 10 words is a candidate; each span gets a mention score and each pair an antecedent score, and a span chooses among all earlier spans plus a dummy antecedent fixed at score 0 (no link, or not a mention). Span… 2. **[Deep Semantic Role Labeling: What Works and What's Next](https://md.research.elamur.ai/free/en/papers/he-2017-deep-semantic-role-labeling-what/)** *(2017)* A neural SRL model that treats each predicate's arguments as BIO tags over the sentence, with no syntax input. 3. **[LSTM CCG Parsing](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-lewis-lstm-ccg-parsing/)** *(2016)* The paper asks whether a CCG parser needs an explicit model of bilexical dependencies. Its parser has none: a bidirectional LSTM supertagger assigns lexical categories, a deterministic grammar combines them, and A* search finds the best parse. Semi-supervised tri-training adds training signal. On CCGbank section 23… 4. **[Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language](https://md.research.elamur.ai/free/en/papers/he-2015-question-answer-driven-semantic-role/)** *(2015)* The paper introduces question-answer driven semantic role labeling (QA-SRL): each verbal predicate is labeled with wh-questions, such as "Who finished something?", and each question is answered by phrases of the sentence. 5. **[A* CCG Parsing with a Supertag-factored Model](https://md.research.elamur.ai/free/en/papers/aclbulk-2014-lewis-ccg-parsing-supertag-factored-model/)** *(2014)* The question is whether CCG parsing needs a full model of derivations and dependencies, or whether a model of lexical categories is enough. ## Co-authors (4) - [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) ×4 - [Kenton Lee](https://md.research.elamur.ai/free/en/authors/kenton-lee/) ×3 - [Luheng He](https://md.research.elamur.ai/free/en/authors/luheng-he/) ×3 - [Mark Steedman](https://md.research.elamur.ai/free/en/authors/mark-steedman/) ## More papers in the library (16) Pages of the Research library: our summary, the ideas, references and who cites each. - [Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/) — Weijia Shi, Xiaochuang Han, Mike Lewis, et al. · 2024 - [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/) — Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al. · 2023 - [In-context Examples Selection for Machine Translation](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/) — Sweta Agrawal, Chunting Zhou, Mike Lewis, et al. · 2023 - [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/) — Xi Victoria Lin, Xilun Chen, Mingda Chen, et al. · 2023 - [REPLUG: Retrieval-Augmented Black-Box Language Models](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/) — Weijia Shi, Sewon Min, Michihiro Yasunaga, et al. · 2023 - [Self-Alignment with Instruction Backtranslation](https://md.research.elamur.ai/free/en/papers/li-2023-self-alignment-instruction-backtranslation/) — Xian Li, Ping Yu, Chunting Zhou, et al. · 2023 - [Contrastive Decoding: Open-ended Text Generation as Optimization](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/) — Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al. · 2022 - [Improving Passage Retrieval with Zero-Shot Question Generation](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/) — Devendra Sachan, Mike Lewis, Mandar Joshi, et al. · 2022 - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) — Sewon Min, Xinxi Lyu, Ari Holtzman, et al. · 2022 - [8-bit Optimizers via Block-wise Quantization](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/) — Tim Dettmers, Mike Lewis, Sam Shleifer, et al. · 2021 - [Noisy Channel Language Model Prompting for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/min-2021-noisy-channel-language-model-prompting/) — Sewon Min, Mike Lewis, Hannaneh Hajishirzi, et al. · 2021 - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) — Mike Lewis, Yinhan Liu, Naman Goyal, et al. · 2020 - [Pre-training via Paraphrasing](https://md.research.elamur.ai/free/en/papers/lewis-2020-pre-training-paraphrasing/) — Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, et al. · 2020 - [Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-transfer-learning-multilingual/) — Sebastian Schuster, Sonal Gupta, Rushin Shah, et al. · 2019 - [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://md.research.elamur.ai/free/en/papers/gupta-2018-semantic-parsing-task-oriented-dialog/) — Sonal Gupta, Rushin Shah, Mrinal Mohit, et al. · 2018 - [Deal or No Deal? End-to-End Learning of Negotiation Dialogues](https://md.research.elamur.ai/free/en/papers/lewis-2017-deal-or-no-deal-end/) — Mike Lewis, Denis Yarats, Yann Dauphin, et al. · 2017