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Chris Dyer

5 paper cards in Mova's library · 2014–2018

Chris Dyer wrote 5 of the papers Mova reads, in 2014–2018, most of them on Dependency parsing, Machine translation; often with Noah A. Smith, Archna Bhatia, Austin Matthews.

OpenAlex · h-index 76 · 37927 citations · 275 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: Dependency parsing 2 · Machine translation 1

Papers

  1. The NarrativeQA Reading Comprehension Challenge (2018)
    NarrativeQA pairs 1,567 long stories (half Gutenberg books, half movie scripts) with plot summaries from Wikipedia; annotators who saw only the summary wrote about 30 question–answer pairs per story in their own words (46,765 in all, answers 4.7 tokens on average, a second reference answer per question).
  2. Ontology-Aware Token Embeddings for Prepositional Phrase Attachment (2017)
    A type-level embedding gives one vector per word form, so "pool" as a place and "pool" as a game share a vector.
  3. Generation from Abstract Meaning Representation using Tree Transducers (2016)
    The paper generates English from AMR graphs in two steps. A breadth-first spanning tree is taken from the graph, and a weighted tree-to-string transducer turns that tree into a sentence. The transducer rules are extracted from AMR-aligned sentences: each aligned subgraph becomes a rule with argument slots for its…
  4. Transition-Based Dependency Parsing with Stack Long Short-Term Memory (2015)
    The paper introduces the stack LSTM: an LSTM with a stack pointer, so that pushes and pops take constant time and the network keeps a vector summary of the current stack contents.
  5. A Dependency Parser for Tweets (2014)
    Tweets break the assumptions of newswire parsers: many tokens (retweet markers, usernames, hashtags, URLs, punctuation) have no syntactic role, a tweet can hold several utterances with their own roots, and multiword names and idioms are common.

Co-authors (19)

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