Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation
Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently. However, during training time, typically only one reference is considered for each example, even though there are often multiple references available, e.g., 4 references in NIST MT evaluations, and 5 references in image captioning data. We first investigate several different ways of utilizing multiple human references during training. But more importantly, we then propose an algorithm to generate exponentially many pseudo-references by first compressing existing human references into lattices and then traversing them to generate new pseudo-references. These approaches lead to substantial improvements over strong baselines in both machine translation (+1.5 BLEU) and image captioning (+3.1 BLEU / +11.7 CIDEr).
Code (0)
등록된 구현이 없습니다.
Tasks
Image CaptioningMachine TranslationText GenerationTranslationSimilar Papers 제목 키워드 기반
Filtering Pseudo-References by Paraphrasing for Automatic Evaluation of Machine Translation
In this paper, we introduce our participation in the WMT 2019 Metric Shared Task. We propose an improved version of sentence BLEU using filtered pseudo-references. We propose a method to filter pseudo-references by parap…
Machine TranslationSentenceTranslationThe CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References
Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings
Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay. However, due to the lack of large-scale, …
SentenceTranslationAlleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation
Quality Estimation (QE) models evaluate the quality of machine translations without reference translations, serving as the reward models for the translation task. Due to the data scarcity, synthetic data generation has e…
Machine TranslationSynthetic Data GenerationTranslationUSCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation
The vast majority of evaluation metrics for machine translation are supervised, i.e., (i) are trained on human scores, (ii) assume the existence of reference translations, or (iii) leverage parallel data. This hinders th…
Machine TranslationParallel Corpus MiningSentenceSentence Embeddings+1