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Dan Roth

10 paper cards in Mova's library · 2004–2020

Dan Roth wrote 10 of the papers Mova reads, in 2004–2020, most of them on Coreference resolution, Semantic role labeling; often with Subhro Roy, Ezra Daya, Shuly Wintner.

OpenAlex · ORCID · h-index 85 · 31311 citations · 731 works — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Coreference resolution 1 · Semantic role labeling 1

Ideas Mova took (1)

From the ideas ledger: an idea tried, kept or queued, or measured with no gain, and what it gave.

Papers

  1. TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions (2020) 1 idea kept
    TORQUE asks temporal relations as reading-comprehension questions instead of labelling event pairs with Allen-style relations.
  2. "I'd rather just go to bed": Understanding Indirect Answers (2020)
    The paper builds Circa, 34,268 pairs of a polar question and an indirect answer (no yes/no words), in ten everyday scenarios, written and labelled by crowd workers.
  3. QANom: Question-Answer driven SRL for Nominalizations (2020)
    QANom extends QA-SRL from verbs to nouns derived from verbs: a noun like proof or increase is questioned with its verb's question templates («What has someone proved?», «Why did something increase?») and answered with spans of the sentence.
  4. Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (2018)
    MultiRC has about 6,000 verified multi-sentence questions (9,872 collected) on 871 paragraphs from seven domains, fiction (Gutenberg, MCTest children's stories, movie plots) being the largest.
  5. Mapping to Declarative Knowledge for Word Problem Solving (2018)
    Instead of learning each arithmetic operation directly from text, this solver first picks a mathematical concept for every operation in the expression tree and then a specific declarative rule under that concept, which fixes the operation.
  6. A Distributional and Orthographic Aggregation Model for English Derivational Morphology (2018)
    The task is derived word generation: given a root word and a transformation (for example "someone who runs" from "run" by AGENT), produce the derived word ("runner").
  7. Unit Dependency Graph and its Application to Arithmetic Word Problem Solving (2017) measured
    The paper adds explicit reasoning about units of measure to an arithmetic word-problem solver. It introduces the Unit Dependency Graph (UDG): one node per quantity plus one for the question, where a node can be marked as a rate ("40 miles per hour", or implicitly "each student has 3 books") with a numerator and a…
  8. Solving General Arithmetic Word Problems (2015) measured
    The paper solves multi-step arithmetic word problems without predefined equation templates by representing the answer as an expression tree in which each number from the text is used at most once.
  9. Identifying Semitic Roots: Machine Learning with Linguistic Constraints (2008)
    Semitic words are built from a root (usually three consonants) and a pattern, and the root is hard to recover from unpointed text because of weak paradigms, missing vowels and attached prefixes.
  10. Learning Hebrew Roots: Machine Learning with Linguistic Constraints (2004)
    The root of a Hebrew word (usually three consonants) is hard to find, because vowels are unwritten, particles attach without spaces, and weak roots change their shape.

Co-authors (23)

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