Heming Ge


2019

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Hierarchical Document Encoder for Parallel Corpus Mining
Mandy Guo | Yinfei Yang | Keith Stevens | Daniel Cer | Heming Ge | Yun-hsuan Sung | Brian Strope | Ray Kurzweil
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual sentence embeddings; (ii) a neural bag-of-words (BoW) document encoding model; (iii) a hierarchical multilingual document encoder (HiDE) that builds on our sentence-level model. The results show document embeddings derived from sentence-level averaging are surprisingly effective for clean datasets, but suggest models trained hierarchically at the document-level are more effective on noisy data. Analysis experiments demonstrate our hierarchical models are very robust to variations in the underlying sentence embedding quality. Using document embeddings trained with HiDE achieves the state-of-the-art on United Nations (UN) parallel document mining, 94.9% P@1 for en-fr and 97.3% P@1 for en-es.

2018

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Learning Semantic Textual Similarity from Conversations
Yinfei Yang | Steve Yuan | Daniel Cer | Sheng-yi Kong | Noah Constant | Petr Pilar | Heming Ge | Yun-Hsuan Sung | Brian Strope | Ray Kurzweil
Proceedings of The Third Workshop on Representation Learning for NLP

We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational responses. The resulting sentence embeddings perform well on the Semantic Textual Similarity (STS) Benchmark and SemEval 2017’s Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training, combining conversational response prediction and natural language inference. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS Benchmark and is competitive with the state-of-the-art feature engineered and mixed systems for both tasks.

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Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
Mandy Guo | Qinlan Shen | Yinfei Yang | Heming Ge | Daniel Cer | Gustavo Hernandez Abrego | Keith Stevens | Noah Constant | Yun-Hsuan Sung | Brian Strope | Ray Kurzweil
Proceedings of the Third Conference on Machine Translation: Research Papers

This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings. Our embedding models are trained to produce similar representations exclusively for bilingual sentence pairs that are translations of each other. This is achieved using a novel training method that introduces hard negatives consisting of sentences that are not translations but have some degree of semantic similarity. The quality of the resulting embeddings are evaluated on parallel corpus reconstruction and by assessing machine translation systems trained on gold vs. mined sentence pairs. We find that the sentence embeddings can be used to reconstruct the United Nations Parallel Corpus (Ziemski et al., 2016) at the sentence-level with a precision of 48.9% for en-fr and 54.9% for en-es. When adapted to document-level matching, we achieve a parallel document matching accuracy that is comparable to the significantly more computationally intensive approach of Uszkoreit et al. (2010). Using reconstructed parallel data, we are able to train NMT models that perform nearly as well as models trained on the original data (within 1-2 BLEU).