Mova0.5 pre-release⌕
☰
home / docs / papers / authors / Kevin Knight

Kevin Knight

11 paper cards in Mova's library · 1991–2016

Kevin Knight wrote 11 of the papers Mova reads, in 1991–2016, most of them on Machine translation, Semantic role labeling; often with Daniel Marcu, Ulf Hermjakob, Jonathan Graehl.

OpenAlex · ORCID · h-index 83 · 24348 citations · 407 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: Machine translation 11 · 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. Generating English from Abstract Meaning Representations (2016)
    The paper treats AMR-to-English generation as phrase-based machine translation, with the AMR first turned into a flat token string in an English-like order.
  2. Aligning English Strings with Abstract Meaning Representation Graphs (2014)
    The paper aligns the tokens of an English sentence to the concepts and relations of its AMR graph, as a step toward learning semantic interpretation and generation rules.
  3. Abstract Meaning Representation for Sembanking (2013)
    The paper presents Abstract Meaning Representation (AMR), a rooted, labelled graph for the meaning of whole sentences, meant to be easy for people to read and for programs to traverse.
  4. Semantics-Based Machine Translation with Hyperedge Replacement Grammars (2012)
    The paper translates through a graph-shaped meaning representation. A source sentence is analysed into a graph, the graph is the pivot, and the target sentence is generated from it. The grammar is a weighted synchronous hyperedge replacement grammar. Its rules are learned from aligned string and graph pairs by an…
  5. Scalable Inference and Training of Context-Rich Syntactic Translation Models (2006)
    The paper extends the rule extraction of Galley et al. (2004). Instead of keeping one minimal derivation per sentence pair, it builds a forest of many derivations, so that larger rules with more syntactic context and alternative readings of unaligned words are all available. It puts probabilities on these multi-level…
  6. Statistical Syntax-Directed Translation with Extended Domain of Locality (2006)
    The paper models syntax-directed translation as a tree-to-string transducer whose source side has multi-level trees, so each rule can cover a larger piece of the parse than a one-level rule.
  7. What's in a translation rule? (2004)
    The paper gives a formal meaning to word alignments between a parsed English sentence and its translation.
  8. Training Tree Transducers (2004)
    The paper argues that probabilistic models of language should be written as tree transducers, since the theory of tree automata is well developed and gives tools that string models have.
  9. Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences (2003)
    The authors take groups of 11 English translations of the same Chinese sentence (899 groups) and parse each one.
  10. A Syntax-based Statistical Translation Model (2001)
    The paper builds a translation channel that takes an English parse tree and produces a Japanese string. At each node the channel makes three stochastic choices: it reorders the children, it inserts words left, right or nowhere, and it translates each leaf word. Insertion positions are indexed by the node label and…
  11. Capturing Language-Specific Semantic Distinctions in Interlingua-based MT (1991)
    The paper describes an English-Spanish system (KBNL) that uses a Discourse Representation Structure (DRS) as its interlingua.

Co-authors (31)

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/authors/kevin-knight/