1 |
Abstract Meaning Representation (AMR) Annotation Release 3.0
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Abstract Meaning Representation (AMR) Annotation Release 2.0
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Abstract Meaning Representation (AMR) Annotation Release 2.0 ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Abstract Meaning Representation (AMR) Annotation Release 1.0
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Abstract Meaning Representation (AMR) Annotation Release 1.0 ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Induction of Word and Phrase Alignments for Automatic Document Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
ISI Chinese-English Automatically Extracted Parallel Text ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
ISI Arabic-English Automatically Extracted Parallel Text ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Scalable Inference and Training of Context-Rich Syntactic Translation Models
|
|
|
|
Abstract:
Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syn- tactic approaches seek to remedy these problems. In this paper, we take the framework for acquiring multi-level syntactic translation rules of (Galley et al., 2004) from aligned tree-string pairs, and present two main extensions of their approach: first, instead of merely computing a single derivation that minimally explains a sentence pair, we construct a large number of derivations that include contextually richer rules, and account for multiple interpretations of unaligned words. Second, we pro- pose probability estimates and a training procedure for weighting these rules. We contrast different approaches on real examples, show that our estimates based on multiple derivations favor phrasal re-orderings that are linguistically better motivated, and establish that our larger rules provide a 3.63 BLEU point increase over minimal rules.
|
|
Keyword:
Computer science; Information technology
|
|
URL: https://doi.org/10.7916/D8H99DP4
|
|
BASE
|
|
Hide details
|
|
17 |
Scalable Inference and Training of Context-Rich Syntactic Translation Models ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|