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.
While these works all adopt PPL as the metric to optimize, sometimes one may optimize a task-specific objective. For example, and propose discriminative LMs to improve speech recognition. The common methodology therein is to fit a probabilistic model, e.g., conditional random field @cite_10 , to the space of text candidates, and maximize the probability at the desired candidate. The problem is often solved by perceptron algorithm. However, these methods all rely on ad-hoc choice of features, e.g., counts of @math -grams where @math varies in a small range (e.g.,1 to 3). Moreover, it is also not clear how these methods would take advantage of an existing language model (trained on large unsupervised corpus). Nevertheless, the same methodology can be extended to RNNLMs, thus avoiding the aforementioned limitations. For example, train an RNNLM by favoring sentences with high BLEU scores and integrate it into a phrase-based MT decoder.
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Recurrent neural network language models (RNNLMs) have recently shown to outperform the venerable n-gram language models (LMs). However, in automatic speech recognition (ASR), RNNLMs were not yet used to directly decode a speech signal. Instead, RNNLMs are rather applied to rescore N-best lists generated from word lattices. To use RNNLMs in earlier stages of the speech recognition, our work proposes to transform RNNLMs into weighted finite state transducers approximating their underlying probability distribution. While the main idea consists in discretizing continuous representations of word histories, we present a first implementation of the approach using clustering techniques and entropy-based pruning. Achieved experimental results on LM perplexity and on ASR word error rates are encouraging since the performance of the discretized RNNLMs is comparable to the one of n-gram LMs. This paper proposes an efficient one-pass decoding method for realtime speech recognition employing a recurrent neural network language model (RNNLM). An RNNLM is an effective language model that yields a large gain in recognition accuracy when it is combined with a standard n -gram model. However, since every word probability distribution based on an RNNLM is dependent on the entire history from the beginning of the speech, the search space in Viterbi decoding grows exponentially with the length of the recognition hypothesesand makescomputation prohibitivelyexpensive. Therefore, an RNNLM is usually used by N -best rescoring or by approximating it to a back-off n -gram model. In this paper, we present another approach that enables one-pass Viterbi decoding with an RNNLM without approximation, where the RNNLM is represented as a prefix tree of possible word sequences, and only the part needed for decoding is generated on-the-fly and used to rescore each hypothesis using an on-the-fly composition technique we previously proposed. Experimental results on the MIT lecture transcription task show that our proposed method enables one-pass decoding with small overhead for the RNNLM and achieves a slightly higher accuracy than 1000-best rescoring. Furthermore, it reduces the latency from the end of each utterance in two-pass decoding by a factor of 10. Recurrent neural network language models (RNNLMs) have recently produced improvements on language processing tasks ranging from machine translation to word tagging and speech recognition. To date, however, the computational expense of RNNLMs has hampered their application to first pass decoding. In this paper, we show that by restricting the RNNLM calls to those words that receive a reasonable score according to a n-gram model, and by deploying a set of caches, we can reduce the cost of using an RNNLM in the first pass to that of using an additional n-gram model. We compare this scheme to lattice rescoring, and find that they produce comparable results for a Bing Voice search task. The best performance results from rescoring a lattice that is itself created with a RNNLM in the first pass. In this paper, we investigate and compare three different possibilities to convert recurrent neural network language models (RNNLMs) into backoff language models (BNLM). While RNNLMs often outperform traditional n-gram approaches in the task of language modeling, their computational demands make them unsuitable for an efficient usage during decoding in an LVCSR system. It is, therefore, of interest to convert them into BNLMs in order to integrate their information into the decoding process. This paper compares three different approaches: a text based conversion, a probability based conversion and an iterative conversion. The resulting language models are evaluated in terms of perplexity and mixed error rate in the context of the Code-Switching data corpus SEAME. Although the best results are obtained by combining the results of all three approaches, the text based conversion approach alone leads to significant improvements on the SEAME corpus as well while offering the highest computational efficiency. In total, the perplexity can be reduced by 11.4 relative on the evaluation set and the mixed error rate by 3.0 relative on the same data set. In this paper, we propose to enhance machine translation system combination (MTSC) with a sentence-level paraphrasing model trained by a neural network. This work extends the number of candidates in MTSC by paraphrasing the whole original MT translation sentences. First we train a neural paraphrasing model of Encoder-Decoder, and leverage the model to paraphrase the MT system outputs to generate synonymous candidates in the semantic space. Then we merge all of them into a single improved translation by a state-of-the-art system combination approach (MEMT) adding some new paraphrasing features. Our experimental results show a significant improvement of 0.28 BLEU points on the WMT2011 test data and 0.41 BLEU points without considering the out-of-vocabulary (OOV) words for the sentence-level paraphrasing model. 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. This paper presents methods to accelerate recurrent neural network based language models (RNNLMs) for online speech recognition systems. Firstly, a lossy compression of the past hidden layer outputs (history vector) with caching is introduced in order to reduce the number of LM queries. Next, RNNLM computations are deployed in a CPU-GPU hybrid manner, which computes each layer of the model on a more advantageous platform. The added overhead by data exchanges between CPU and GPU is compensated through a frame-wise batching strategy. The performance of the proposed methods evaluated on LibriSpeech 1 test sets indicates that the reduction in history vector precision improves the average recognition speed by 1.23 times with minimum degradation in accuracy. On the other hand, the CPU-GPU hybrid parallelization enables RNNLM based real-time recognition with a four times improvement in speed. This paper shows that discriminative reranking with an averaged perceptron model yields substantial improvements in realization quality with CCG. The paper confirms the utility of including language model log probabilities as features in the model, which prior work on discriminative training with log linear models for HPSG realization had called into question. The perceptron model allows the combination of multiple n-gram models to be optimized and then augmented with both syntactic features and discriminative n-gram features. The full model yields a state-of-the-art BLEU score of 0.8506 on Section 23 of the CCGbank, to our knowledge the best score reported to date using a reversible, corpus-engineered grammar. Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate. Minimum Error Rate Training (MERT) is an effective means to estimate the feature function weights of a linear model such that an automated evaluation criterion for measuring system performance can directly be optimized in training. To accomplish this, the training procedure determines for each feature function its exact error surface on a given set of candidate translations. The feature function weights are then adjusted by traversing the error surface combined over all sentences and picking those values for which the resulting error count reaches a minimum. Typically, candidates in MERT are represented as N-best lists which contain the N most probable translation hypotheses produced by a decoder. In this paper, we present a novel algorithm that allows for efficiently constructing and representing the exact error surface of all translations that are encoded in a phrase lattice. Compared to N-best MERT, the number of candidate translations thus taken into account increases by several orders of magnitudes. The proposed method is used to train the feature function weights of a phrase-based statistical machine translation system. Experiments conducted on the NIST 2008 translation tasks show significant runtime improvements and moderate BLEU score gains over N-best MERT. This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that despite being more costly to train, RNNLMs obtain much lower perplexities on standard benchmarks than n-gram models. We train the largest known RNNs and present relative word error rates gains of 18 on an ASR task. We also present the new lowest perplexities on the recently released billion word language modelling benchmark, 1 BLEU point gain on machine translation and a 17 relative hit rate gain in word prediction. This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5 and 11.3 F-1 score improvement over co-training on disease and virus NER respectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better performance compared to using word embeddings. Speech recognition in a realistic noisy environment using multiple microphones is the focal point of the third CHiME challenge. Over the baseline ASR system provided for this challenge, we apply state of the art algorithms for boosting acoustic model learning and hypothesis rescoring to improve the final output. To this aim, we first use the automatic transcription of each channel to re-train the acoustic model for that channel and then we apply linear language model rescoring to find a better solution in the n-best list. LM rescoring is performed using an efficient set of N-gram and Recurrent Neural Network LM (RNNLM) trained on a wisely-selected text set. In the experiments, we show that the proposed approach improves not only the individual channel transcription, but also the enhanced channels produced by MVDR and delay-and-sum beamforming. While large-scale discriminative training has triumphed in many NLP problems, its definite success on machine translation has been largely elusive. Most recent efforts along this line are not scalable (training on the small dev set with features from top 100 most frequent words) and overly complicated. We instead present a very simple yet theoretically motivated approach by extending the recent framework of “violation-fixing perceptron”, using forced decoding to compute the target derivations. Extensive phrase-based translation experiments on both Chinese-to-English and Spanish-to-English tasks show substantial gains in BLEU by up to +2.3 +2.0 on dev test over MERT, thanks to 20M+ sparse features. This is the first successful effort of large-scale online discriminative training for MT. A fundamental problem in text generation is word ordering. Word ordering is a computationally difficult problem, which can be constrained to some extent for particular applications, for example by using synchronous grammars for statistical machine translation. There have been some recent attempts at the unconstrained problem of generating a sentence from a multi-set of input words (, 2009; Zhang and Clark, 2011). By using CCG and learning guided search, Zhang and Clark reported the highest scores on this task. One limitation of their system is the absence of an N-gram language model, which has been used by text generation systems to improve fluency. We take the Zhang and Clark system as the baseline, and incorporate an N-gram model by applying online large-margin training. Our system significantly improved on the baseline by 3.7 BLEU points. Generative Adversarial Networks (GANs) are a promising approach to language generation. The latest works introducing novel GAN models for language generation use n-gram based metrics for evaluation and only report single scores of the best run. In this paper, we argue that this often misrepresents the true picture and does not tell the full story, as GAN models can be extremely sensitive to the random initialization and small deviations from the best hyperparameter choice. In particular, we demonstrate that the previously used BLEU score is not sensitive to semantic deterioration of generated texts and propose alternative metrics that better capture the quality and diversity of the generated samples. We also conduct a set of experiments comparing a number of GAN models for text with a conventional Language Model (LM) and find that neither of the considered models performs convincingly better than the LM. We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modification of the proof of convergence of the perceptron algorithm for classification problems. We give experimental results on part-of-speech tagging and base noun phrase chunking, in both cases showing improvements over results for a maximum-entropy tagger. Domain adaptation has recently gained interest in statistical machine translation to cope with the performance drop observed when testing conditions deviate from training conditions. The basic idea is that in-domain training data can be exploited to adapt all components of an already developed system. Previous work showed small performance gains by adapting from limited in-domain bilingual data. Here, we aim instead at significant performance gains by exploiting large but cheap monolingual in-domain data, either in the source or in the target language. We propose to synthesize a bilingual corpus by translating the monolingual adaptation data into the counterpart language. Investigations were conducted on a state-of-the-art phrase-based system trained on the Spanish--English part of the UN corpus, and adapted on the corresponding Europarl data. Translation, re-ordering, and language models were estimated after translating in-domain texts with the baseline. By optimizing the interpolation of these models on a development set the BLEU score was improved from 22.60 to 28.10 on a test set. One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect @math -grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call "translation pieces". We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation. 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.