We propose a large margin criterion for training neural language models. Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. However, we demonstrate that PPL may not be the best metric to optimize in some tasks, and further propose a large margin formulation. The proposed method aims to enlarge the margin between the "good" and "bad" sentences in a task-specific sense. It is trained end-to-end and can be widely applied to tasks that involve re-scoring of generated text. Compared with minimum-PPL training, our method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation.
If we cast the problem of picking the best text sequence as a ranking problem, the aforementioned works can be considered as pointwise" learning-to-rank approaches @cite_24 . In contrast, the proposed method is a pairwise" approach @cite_25 , as it learns a neural language model by comparison between pairs of sentences. Earlier works in this fashion may date back to @cite_19 , which improves a semantic parser. Learning by pairwise comparison" is also seen in several MT literatures. For example, propose to train a phrase-based MT system by minimizing a pairwise ranking loss. optimize the beam search process in a Neural Machine Translation (NMT) system. They enforce the score of a reference to be higher than that of its decoded k-th candidate by at least a unit margin.
@cite_24 @cite_19 @cite_25
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Current syntactic machine translation MT systems implicitly use beam-width unlimited search in learning model parameters e.g., feature values for each translation rule. However, a limited beam-width has to be adopted in decoding new sentences, and the MT output is in general evaluated by various metrics, such as BLEU and TER. In this paper, we address: 1 the mismatch of adopted beam-widths between training and decoding; and 2 the mismatch of training criteria and MT evaluation metrics. Unlike previous work, we model the two problems in a single training paradigm simultaneously. We design a loss-augmented approach that explicitly considers the limited beam-width and evaluation metric in training, and present a simple but effective method to learn the model. By using beam search and BLEU-related losses, our approach improves a state-of-the-art syntactic MT system by +1.0 BLEU on Chinese-to-English and English-to-Chinese translation tasks. It even outperforms seven previous training approaches over 0.8 BLEU points. More interestingly, promising improvements are observed when our approach works with TER. In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German->English news domain and English->Russian e-commerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3 BLEU absolute as compared to a strong NMT baseline. Preference learning is an emerging topic that appears in different guises in the recent literature. This work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a mapping, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data using a natural extension of pairwise classification. A ranking is then derived from the preference relation thus obtained by means of a ranking procedure, whereby different ranking methods can be used for minimizing different loss functions. In particular, we show that a simple (weighted) voting strategy minimizes risk with respect to the well-known Spearman rank correlation. We compare RPC to existing label ranking methods, which are based on scoring individual labels instead of comparing pairs of labels. Both empirically and theoretically, it is shown that RPC is superior in terms of computational efficiency, and at least competitive in terms of accuracy.