In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.
While both our approach and use Auxiliary Context mechanism for incorporating cross-sentence context, there are two main differences: 1) we have separate parameters to better control the effects of the cross- and intra-sentence contexts, while they only have one parameter matrix to manage the single representation that encodes both contexts; 2) based on the intuition that not every target word generation requires equivalent cross-sentence context, we introduce a context gate @cite_5 to control the amount of information from it, while they don't.
@cite_5
2009987829 2950594802 2415204069 2964298349
The ability to generate or to recognize paraphrases is key to the vast majority of NLP applications. As correctly exploiting context during translation has been shown to be successful, using context information for paraphrasing could also lead to improved performance. In this article, we adopt the pivot approach based on parallel multilingual corpora proposed by (Bannard and Callison-Burch, 2005), which finds short paraphrases by finding appropriate pivot phrases in one or several auxiliary languages and back-translating these pivot phrases into the original language. We show how context can be exploited both when attempting to find pivot phrases, and when looking for the most appropriate paraphrase in the original subsentential "envelope". This framework allows the use of paraphrasing units ranging from words to large sub-sentential fragments for which context information from the sentence can be successfully exploited. We report experiments on a text revision task, and show that in these experiments our contextual sub-sentential paraphrasing system outperforms a strong baseline system. While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remain vague. We observe that whether or not an algorithm uses a particular feature set (sentence IDs) accounts for a significant performance gap among these algorithms. This feature set is also used by traditional alignment algorithms, such as IBM Model-1, which demonstrate similar performance to state-of-the-art embedding algorithms on a variety of benchmarks. Overall, we observe that different algorithmic approaches for utilizing the sentence ID feature space result in similar performance. This paper draws both empirical and theoretical parallels between the embedding and alignment literature, and suggests that adding additional sources of information, which go beyond the traditional signal of bilingual sentence-aligned corpora, may substantially improve cross-lingual word embeddings, and that future baselines should at least take such features into account. In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a first-stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin. In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a translation while target contexts affect the fluency . Intuitively, generation of a content word should rely more on the source context and generation of a functional word should rely more on the target context. Due to the lack of effective control over the influence from source and target contexts, conventional NMT tends to yield fluent but inadequate translations. To address this problem, we propose context gates which dynamically control the ratios at which source and target contexts contribute to the generation of target words. In this way, we can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts. Experiments show that our approach significantly improves upon a standard attention-based NMT system by +2.3 BLEU points.