Non-Monotonic Sentence Alignment via Semisupervised Learning
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalMachine TranslationSentenceSimilar Papers 제목 키워드 기반
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
Non-autoregressive translation (NAT) models are typically trained with the cross-entropy loss, which forces the model outputs to be aligned verbatim with the target sentence and will highly penalize small shifts in word …
Machine TranslationSentenceTranslationSpanAlign: Sentence Alignment Method based on Cross-Language Span Prediction and ILP
We propose a novel method of automatic sentence alignment from noisy parallel documents. We first formalize the sentence alignment problem as the independent predictions of spans in the target document from sentences in …
Machine TranslationSentenceSentence EmbeddingsTranslationMonotonic Simultaneous Translation with Chunk-wise Reordering and Refinement
Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing wi…
Machine TranslationSentenceTranslationWord AlignmentAdaptative Bilingual Aligning Using Multilingual Sentence Embedding
In this paper, we present an adaptive bitextual alignment system called AIlign. This aligner relies on sentence embeddings to extract reliable anchor points that can guide the alignment path, even for texts whose paralle…
SentenceSentence EmbeddingSentence-EmbeddingSentence EmbeddingsTranslate First Reorder Later: Leveraging Monotonicity in Semantic Parsing
Prior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks. This limitation led to a resurgence of methods that model alignments between sentences and their corre…
Semantic Parsing