Context-aware sentence encoding and bidirectional document encoding improve HAN accuracy by up to 0.46 percentage points on three document classification benchmarks.
Context-Aware Neural Machine Translation Learns Anaphora Resolution
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence. We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed. We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation. We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora. It is consistent with gains for sentences where pronouns need to be gendered in translation. Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).
fields
cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Bidirectional Context-Aware Hierarchical Attention Network for Document Understanding
Context-aware sentence encoding and bidirectional document encoding improve HAN accuracy by up to 0.46 percentage points on three document classification benchmarks.