Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field.Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality.Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from the irregular EMR itself without external knowledge bases in this paper.Specifically, we first structure the sequence of the EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph.Features having the strongest causal connection with the results provide interpretive support for the diagnosis.Experimental results on real Chinese EMRs of the lymphedema demonstrate that our method can diagnose four types of EMRs correctly, and can provide accurate supporting facts for the results.More importantly, the results on different diseases demonstrate the robustness of our approach, which represents the potential application in the medical field 1 .

Transcript disfluency detection (TDD) is an important component of the real-time speech translation system, which arouses more and more interests in recent years. This paper presents our study on adapting neural machine translation (NMT) models for TDD. We propose a general training framework for adapting NMT models to TDD task rapidly. In this framework, the main structure of the model is implemented similar to the NMT model. Additionally, several extended modules and training techniques which are independent of the NMT model are proposed to improve the performance, such as the constrained decoding, denoising autoencoder initialization and a TDD-specific training object. With the proposed training framework, we achieve significant improvement. However, it is too slow in decoding to be practical. To build a feasible and production-ready solution for TDD, we propose a fast non-autoregressive TDD model following the non-autoregressive NMT model emerged recently. Even we do not assume the specific architecture of the NMT model, we build our TDD model on the basis of Transformer, which is the state-of-the-art NMT model. We conduct extensive experiments on the publicly available set, Switchboard, and in-house Chinese set. Experimental results show that the proposed model significantly outperforms previous state-ofthe-art models.

Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data.The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure.To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences.Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation.With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.

While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity. To tackle this problem, we propose a novel semi-supervised approach which can utilize large amounts of unlabelled data. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN). In addition, we use the unlabelled corpus to enhance the performance. Besides, the Generative Adversarial Network (GAN) training is applied to enforce the similar distribution between the labelled and unlabelled data. The experimental results show that our approach achieves significant improvements over strong baselines.

Inserting proper punctuation into Automatic Speech Recognizer(ASR) transcription is a challenging and promising task in real-time Spoken Language Translation(SLT). Traditional methods built on the sequence labelling framework are weak in handling the joint punctuation. To tackle this problem, we propose a novel self-attention based network, which can solve the aforementioned problem very well. In this work, a light-weight neural net is proposed to extract the hidden features based solely on self-attention without any Recurrent Neural Nets(RNN) and Convolutional Neural Nets(CNN). We conduct extensive experiments on complex punctuation tasks. The experimental results show that the proposed model achieves significant improvements on joint punctuation task while being superior to traditional methods on simple punctuation task as well.

Impressive neural machine translation (NMT) results are achieved in domains with large-scale, high quality bilingual training corpora. However, transferring to a target domain with significant domain shifts but no bilingual training corpora remains largely unexplored. To address the aforementioned setting of unsupervised domain adaptation, we propose a novel adversarial training procedure for NMT to leverage the widespread monolingual data in target domain. Two discriminative networks, namely the domain discriminator and pair discriminator, are introduced to guide the translation model. The domain discriminator evaluates whether the sentences generated by the translation model are indistinguishable from the ones in target domain. The pair discriminator assesses whether the generated sentences are paired with the source-side sentences. The translation model acts as an adversary to the two discriminators, which aims to generate sentences uneasily discriminated by the discriminators. We tested our approach on Chinese-English and English-German translation tasks. Experimental results show that our approaches achieve great success in unsupervised domain adaptation for NMT.

End-to-end models have been showing superiority in Automatic Speech Recognition (ASR).At the same time, the capacity of streaming recognition has become a growing requirement for end-to-end models.Following these trends, an encoder-decoder recurrent neural network called Recurrent Neural Aligner (RNA) has been freshly proposed and shown its competitiveness on two English ASR tasks.However, it is not clear if RNA can be further improved and applied to other spoken language.In this work, we explore the applicability of RNA in Mandarin Chinese and present four effective extensions: In the encoder, we redesign the temporal downsampling and introduce a powerful convolutional structure.In the decoder, we utilize a regularizer to smooth the output distribution and conduct joint training with a language model.On two Mandarin Chinese conversational telephone speech recognition (MTS) datasets, our Extended-RNA obtains promising performance.Particularly, it achieves 27.7% character error rate (CER), which is superior to current state-of-the-art result on the popular HKUST task.

Zhen Yang, Wei Chen, Feng Wang, Bo Xu. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Neural Machine Translation (NMT) lays intensive burden on computation and memory cost.It is a challenge to deploy NMT models on the devices with limited computation and memory budgets.This paper presents a four stage pipeline to compress model and speed up the decoding for NMT.Our method first introduces a compact architecture based on convolutional encoder and weight shared embeddings.Then weight pruning is applied to obtain a sparse model.Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search.Hence, the time-consuming beam search can be replaced by simple greedy decoding.Finally, vocabulary selection is used to reduce the computation of softmax layer.Our final model achieves 10× speedup, 17× parameters reduction, <35MB storage size and comparable performance compared to the baseline model.

Neural machine translation (NMT) has shown promising results and rapidly gained adoption in many large-scale settings. With the NMT model being widely used in empirical productions, its long-standing weakness in handling the rare and out of vocabulary words has been amplified a lot. In order to release the model from the stress of “understanding” the rare words, copy mechanism has been proposed to deal with the rare and unseen words for the neural network models using attention. However the negative side of the copy mechanism is that the model is only able to decide whether to copy or not. It is unable to detect which class should the rare word be copied to, such as person, location, and organization. This paper deeply investigates this limitation of the NMT model. As a result, we propose a new NMT model by novelly incorporating a class-specific copy network. With the network, the proposed NMT model is able to decide which class the words in the target belong to and which class in the source should be copied to. Experimental results on Chinese-English translation tasks show that the proposed model outperforms the traditional NMT model with a large margin especially for sentences containing the rare words.

Attention mechanism advances the neural machine translation (NMT) by reducing the confusion introduced by irrelevant words in long sentences. However, the confusion caused by ambiguous words hasn't been handled yet and it may be a bottleneck for the NMT model. This paper validates the hypothesis and proposes a simple and flexible framework, which enables the NMT model to only focus on the relevant sense type of the input word in current context. Experiments show that the proposed model achieves substantial improvements on every test set over competitive baselines. Our contributions come from twofold. Firstly, to the best of our knowledge, this is the first effort to introduce the multi-sense representation, which represents each sense type of the word with a sense-specific embedding, into NMT. Secondly, We propose a sense search module which can detect the sense type of the word automatically. Flexibility and versatility are the most attractive characteristic of the proposed sense search module. It can be applied to any other semantic related NLP tasks with little modification.

This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words. On the use of row convolution (Amodei et al., 2015), the encoder of the proposed model composes word-level information from the input sequences of characters automatically. Since our model doesn’t rely on the boundaries between each word (as the whitespace boundaries in English), it is also applied to languages without explicit word segmentations (like Chinese). Experimental results on Chinese-English translation tasks show that the proposed character-aware NMT model can achieve comparable translation performance with the traditional word based NMT models. Despite the target side is still word based, the proposed model is able to generate much less unknown words.

This paper presents a bilingual semisupervised Chinese word segmentation (CWS) method that leverages the natural segmenting information of English sentences.The proposed method involves learning three levels of features, namely, character-level, phrase-level and sentence-level, provided by multiple submodels.We use a sub-model of conditional random fields (CRF) to learn monolingual grammars, a sub-model based on character-based alignment to obtain explicit segmenting knowledge, and another sub-model based on transliteration similarity to detect out-of-vocabulary (OOV) words.Moreover, we propose a sub-model leveraging neural network to ensure the proper treatment of the semantic gap and a phrase-based translation sub-model to score the translation probability of the Chinese segmentation and its corresponding English sentences.A cascaded log-linear model is employed to combine these features to segment bilingual unlabeled data, the results of which are used to justify the original supervised CWS model.The evaluation shows that our method results in superior results compared with those of the state-of-the-art monolingual and bilingual semi-supervised models that have been reported in the literature.

Hierarchical phrase-based translation models have advanced statistical machine translation (SMT). Because such models can improve leveraging of syntactic information, two types of methods (leveraging source parsing and leveraging shallow parsing) are applied to introduce syntactic constraints into translation models. In this paper, we propose a bilingually-constrained recursive neural network (BC-RNN) model to combine the merits of these two types of methods. First we perform supervised learning on a manually parsed corpus using the standard recursive neural network (RNN) model. Then we employ unsupervised bilingually-constrained tuning to improve the accuracy of the standard RNN model. Leveraging the BC-RNN model, we introduce both source parsing and shallow parsing information into a hierarchical phrase-based translation model. The evaluation demonstrates that our proposed method outperforms other state-of-the-art statistical machine translation methods for National Institute of Standards and Technology 2008 (NIST 2008) Chinese-English machine translation testing data.