--- title: "Chris Callison-Burch — papers, page 2 — Elamur Research" description: "Chris Callison-Burch: 40 papers on Elamur Research (2005–2026) — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/chris-callison-burch/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/chris-callison-burch/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Chris Callison-Burch](https://md.research.elamur.ai/free/en/authors/chris-callison-burch/) / page 2 # Chris Callison-Burch · page 2 40 papers in our library · 2005–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/chris-callison-burch/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/chris-callison-burch/) [1](https://md.research.elamur.ai/free/en/authors/chris-callison-burch/) **2** 26. **[Joshua: An Open Source Toolkit for Parsing-Based Machine Translation](https://md.research.elamur.ai/free/en/papers/li-2009-joshua-open-source-toolkit-parsing/)** *(2009 · 163 citations)* Parsing-based statistical MT (Hiero-style hierarchical models and other syntax-driven approaches) had advanced rapidly. 27. **[Findings of the 2011 Workshop on Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/burch-2011-findings-2011-workshop-statistical-machine/)** *(2011 · 136 citations)* This report documents the outcomes of the WMT11 shared tasks: translation between English and Czech, French, German and Spanish, combining outputs of several MT systems, and evaluation metrics judged against human preferences. 28. **[A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic](https://md.research.elamur.ai/free/en/papers/cotterell-2014-multi-dialect-multi-genre-corpus/)** *(2014 · 128 citations)* Most Arabic NLP tools and corpora target Modern Standard Arabic (MSA), but online user-generated text is largely dialectal. 29. **[A Recipe For Arbitrary Text Style Transfer with Large Language Models](https://md.research.elamur.ai/free/en/papers/reif-2021-recipe-arbitrary-text-style-transfer/)** *(2021 · 127 citations)* Most text style transfer systems depend on labeled data, either parallel sentence pairs or corpora in a single style. 30. **[Constructing Corpora for the Development and Evaluation of Paraphrase Systems](https://md.research.elamur.ai/free/en/papers/cohn-2008-constructing-corpora-development-evaluation-paraphrase/)** *(2008 · 118 citations)* Paraphrase acquisition research has had no shared benchmark. Systems were usually judged by people rating sample outputs, which is slow, hard to reproduce and says almost nothing about recall. Cohn, Callison-Burch and Lapata address this gap by building a monolingual parallel corpus in which paraphrases are made… 31. **[RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://md.research.elamur.ai/free/en/papers/dugan-2024-raid-shared-benchmark-robust-evaluation/)** *(2024 · 110 citations)* Vendors and researchers often claim near-perfect accuracy for detecting AI-written text. These claims are rarely checked on common data, and existing benchmarks are too easy: they seldom vary decoding settings or include adversarial manipulation. The authors address this with RAID, a benchmark of about 6.2 million… 32. **[Creating Speech and Language Data With Amazon's Mechanical Turk](https://md.research.elamur.ai/free/en/papers/burch-2010-creating-speech-language-data-amazon/)** *(2010 · 86 citations)* This paper introduces the NAACL-2010 workshop on using Amazon's Mechanical Turk (MTurk) to build data for speech and language technology, and it also serves as a practical guide to the platform. 33. **[Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT](https://md.research.elamur.ai/free/en/papers/baker-2012-modality-negation-simt-use-modality/)** *(2012 · 77 citations)* The paper asks whether semantic knowledge can improve statistical machine translation for a low-resource pair, Urdu–English, when training data is sparse. 34. **[FrameNet+: Fast Paraphrastic Tripling of FrameNet](https://md.research.elamur.ai/free/en/papers/pavlick-2015-framenet-fast-paraphrastic-tripling-framenet/)** *(2015 · 75 citations)* FrameNet is a manually built lexical-semantic resource of nearly 1,000 frames. Its practical value is limited because many words in real text are not listed as lexical units (LUs) for any frame. In the authors' New York Times sample, fewer than half of the targets could be matched to a correct frame. To address this… 35. **[FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-fanoutqa-multi-hop-multi-document/)** *(2024 · 51 citations)* Many real-world requests are "fan-out" questions. To answer one, a system must first identify a set of entities and then gather and combine facts about each from separate documents. Existing benchmarks such as HotpotQA, LongBench and ZeroSCROLLS mostly test reasoning within one document or across only a few. FanOutQA… 36. **[CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization](https://md.research.elamur.ai/free/en/papers/majumder-2023-clin-continually-learning-language-agent/)** *(2023 · 34 citations)* Agents built on LLMs can act in text-based simulated worlds without costly reinforcement learning. Reflection-based methods such as Reflexion can retry a single task better, but their lessons tend to be tied to one setting and do not accumulate. CLIN (continual learning from interactions) addresses this. The model's… 37. **[Translations of the Callhome Egyptian Arabic corpus for conversational speech translation](https://md.research.elamur.ai/free/en/papers/kumar-2014-translations-callhome-egyptian-arabic-corpus/)** *(2014 · 28 citations)* Research on translating conversational speech has been held back by the lack of in-domain data. For Egyptian Arabic in particular, no resource aligned speech, transcripts and English translations from the same domain. As a result, translation systems were usually trained on scripted or written text that differs… 38. **[Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?](https://md.research.elamur.ai/free/en/papers/patel-2022-low-resource-authorship-style-transfer/)** *(2022 · 25 citations)* The paper defines low-resource authorship style transfer: rewriting text in the style of a target author who has only about 16 posts, roughly 500 tokens. 39. **[Learning Scalar Adjective Intensity from Paraphrases](https://md.research.elamur.ai/free/en/papers/cocos-2018-learning-scalar-adjective-intensity-paraphrases/)** *(2018 · 21 citations)* The paper orders scalar adjectives (warm, hot, scalding) by intensity from paraphrase pairs such as "really hot" ↔ "scalding". 40. **[NSF-SciFy: Mining the NSF Awards Database for Scientific Claims](https://md.research.elamur.ai/free/en/papers/rao-2026-nsf-scify-mining-nsf-awards/)** *(2026 · 2 citations)* Existing datasets for checking scientific claims are small, number in the thousands of claims, and mostly cover biomedicine or health, drawing on published papers or fact-checking sources. 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