Multi-Source Syntactic Neural Machine Translation

We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses. This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; the resulting representations are then combined using a hierarchical attention mechanism. The proposed model improves over both seq2seq and parsed baselines by over 1 BLEU on the WMT17 English-German task. Further analysis shows that our multi-source syntactic model is able to translate successfully without any parsed input, unlike standard parsed methods. In addition, performance does not deteriorate as much on long sentences as for the baselines.Comment: EMNLP 201

Moving beyond parallel data for neural machine translation

The goal of neural machine translation (NMT) is to build an end-to-end system that automatically translates sentences from the source language to the target language. Neural machine translation has become the dominant paradigm in machine translation in recent years, showing strong improvements over prior statistical methods in many scenarios. However, neural machine translation relies heavily on parallel corpora for training; even for two languages with abundant monolingual resources (or with a large number of speakers), such parallel corpora may be scarce. Thus, it is important to develop methods for leveraging additional types of data in NMT training. This thesis explores ways of augmenting the parallel training data of neural machine translation with non-parallel sources of data. We concentrate on two main types of additional data: monolingual corpora and structural annotations. First, we propose a method for adding target-language monolingual data into neural machine translation in which the monolingual data is converted to parallel data through copying. Thus, the NMT system is trained on two tasks: translation from source language to target language, and autoencoding the target language. We show that this model achieves improvements in BLEU score for low- and medium-resource setups. Second, we consider the task of zero-resource NMT, where no source ↔ target parallel training data is available, but parallel data with a pivot language is abundant. We improve these models by adding a monolingual corpus in the pivot language, translating this corpus into both the source and the target language to create a pseudo-parallel source-target corpus. In the second half of this thesis, we turn our attention to syntax, introducing methods for adding syntactic annotation of the source language into neural machine translation. In particular, our multi-source model, which leverages an additional encoder to inject syntax into the NMT model, results in strong improvements over non-syntactic NMT for a high-resource translation case, while remaining robust to unparsed inputs. We also introduce a multi-task model that augments the transformer architecture with syntax; this model improves translation across several language pairs. Finally, we consider the case where no syntactic annotations are available (such as when translating from very low-resource languages). We introduce an unsupervised hierarchical encoder that induces a tree structure over the source sentences based solely on the downstream task of translation. Although the resulting hierarchies do not resemble traditional syntax, the model shows large improvements in BLEU for low-resource NMT

Translating Phrases in Neural Machine Translation

Phrases play an important role in natural language understanding and machine translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is difficult to integrate them into current neural machine translation (NMT) which reads and generates sentences word by word. In this work, we propose a method to translate phrases in NMT by integrating a phrase memory storing target phrases from a phrase-based statistical machine translation (SMT) system into the encoder-decoder architecture of NMT. At each decoding step, the phrase memory is first re-written by the SMT model, which dynamically generates relevant target phrases with contextual information provided by the NMT model. Then the proposed model reads the phrase memory to make probability estimations for all phrases in the phrase memory. If phrase generation is carried on, the NMT decoder selects an appropriate phrase from the memory to perform phrase translation and updates its decoding state by consuming the words in the selected phrase. Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does. Experiment results on the Chinese to English translation show that the proposed model achieves significant improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201

Towards String-to-Tree Neural Machine Translation

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.Comment: Accepted as a short paper in ACL 201

Chunk-Based Bi-Scale Decoder for Neural Machine Translation

In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.Comment: Accepted as a short paper by ACL 201