Jingbo Zhu

Also published as: JingBo Zhu


2024

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Teaching Language Models to Self-Improve by Learning from Language Feedback
Chi Hu | Yimin Hu | Hang Cao | Tong Xiao | JingBo Zhu
Findings of the Association for Computational Linguistics ACL 2024

Aligning Large Language Models (LLMs) with human intentions and values is crucial yet challenging. Current methods primarily rely on human preferences, which are costly and insufficient in capturing nuanced feedback expressed in natural language. In this paper, we present Self-Refinement Tuning (SRT), a method that leverages model feedback for alignment, thereby reducing reliance on human annotations. SRT uses a base language model (e.g., Tulu2) to generate initial responses, which are critiqued and refined by a more advanced model (e.g., GPT-4-Turbo). This process enables the base model to self-evaluate and improve its outputs, facilitating continuous learning. SRT further optimizes the model by learning from its self-generated feedback and refinements, creating a feedback loop that promotes model improvement. Our empirical evaluations demonstrate that SRT significantly outperforms strong baselines across diverse tasks and model sizes. When applied to a 70B parameter model, SRT increases the win rate from 9.6% to 25.8% on the AlpacaEval 2.0 benchmark, surpassing well-established systems such as GPT-4-0314, Claude 2, and Gemini. Our analysis highlights the crucial role of language feedback in the success of SRT, suggesting potential for further exploration in this direction.

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PartialFormer: Modeling Part Instead of Whole for Machine Translation
Tong Zheng | Bei Li | Huiwen Bao | Jiale Wang | Weiqiao Shan | Tong Xiao | JingBo Zhu
Findings of the Association for Computational Linguistics ACL 2024

The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead. In this work, we emphasize the importance of hidden dimensions in designing lightweight FFNs, a factor often overlooked in previous architectures. Guided by this principle, we introduce PartialFormer, a parameter-efficient Transformer architecture utilizing multiple smaller FFNs to reduce parameters and computation while maintaining essential hidden dimensions. These smaller FFNs are integrated into a multi-head attention mechanism for effective collaboration. We also propose a tailored head scaling strategy to enhance PartialFormer’s capabilities. Furthermore, we present a residual-like attention calculation to improve depth scaling within PartialFormer. Extensive experiments on 9 translation tasks and 1 abstractive summarization task validate the effectiveness of our PartialFormer approach on machine translation and summarization tasks. Our code would be available at: https://github.com/zhengkid/PartialFormer.

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Revisiting Interpolation Augmentation for Speech-to-Text Generation
Chen Xu | Jie Wang | Xiaoqian Liu | Qian Dong | Chunliang Zhang | Tong Xiao | JingBo Zhu | Dapeng Man | Wu Yang
Findings of the Association for Computational Linguistics ACL 2024

Speech-to-text (S2T) generation systems frequently face challenges in low-resource scenarios, primarily due to the lack of extensive labeled datasets. One emerging solution is constructing virtual training samples by interpolating inputs and labels, which has notably enhanced system generalization in other domains. Despite its potential, this technique’s application in S2T tasks has remained under-explored. In this paper, we delve into the utility of interpolation augmentation, guided by several pivotal questions. Our findings reveal that employing an appropriate strategy in interpolation augmentation significantly enhances performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

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Hybrid Alignment Training for Large Language Models
Chenglong Wang | Hang Zhou | Kaiyan Chang | Bei Li | Yongyu Mu | Tong Xiao | Tongran Liu | JingBo Zhu
Findings of the Association for Computational Linguistics ACL 2024

Alignment training is crucial for enabling large language models (LLMs) to cater to human intentions and preferences. It is typically performed based on two stages with different objectives: instruction-following alignment and human-preference alignment. However, aligning LLMs with these objectives in sequence suffers from an inherent problem: the objectives may conflict, and the LLMs cannot guarantee to simultaneously align with the instructions and human preferences well. To response to these, in this work, we propose a Hybrid Alignment Training (Hbat) approach, based on alternating alignment and modified elastic weight consolidation methods. The basic idea is to alternate between different objectives during alignment training, so that better collaboration can be achieved between the two alignment tasks. We experiment with Hbat on summarization and dialogue tasks. Experimental results show that the proposed Hbat can significantly outperform all baselines. Notably, Hbat yields consistent performance gains over the traditional two-stage alignment training when using both proximal policy optimization and direct preference optimization.

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Exploiting Target Language Data for Neural Machine Translation Beyond Back Translation
Abudurexiti Reheman | Yingfeng Luo | Junhao Ruan | Chunliang Zhang | Anxiang Ma | Tong Xiao | JingBo Zhu
Findings of the Association for Computational Linguistics ACL 2024

Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages. To address these issues, researchers have proposed methods to integrate additional knowledge into NMT, such as translation memories (TMs). However, finding TMs that closely match the input sentence remains challenging, particularly in specific domains. On the other hand, monolingual data is widely accessible in most languages, and back-translation is seen as a promising approach for utilizing target language data. Nevertheless, it still necessitates additional training. In this paper, we introduce Pseudo-kNN-MT, a variant of k-nearest neighbor machine translation (kNN-MT) that utilizes target language data by constructing a pseudo datastore. Furthermore, we investigate the utility of large language models (LLMs) for the kNN component. Experimental results demonstrate that our approach exhibits strong domain adaptation capability in both high-resource and low-resource machine translation. Notably, LLMs are found to be beneficial for robust NMT systems.

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EIT: Enhanced Interactive Transformer
Tong Zheng | Bei Li | Huiwen Bao | Tong Xiao | JingBo Zhu
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Two principles: the complementary principle and the consensus principle are widely acknowledged in the literature of multi-view learning. However, the current design of multi-head self-attention, an instance of multi-view learning, prioritizes the complementarity while ignoring the consensus. To address this problem, we propose an enhanced multi-head self-attention (EMHA). First, to satisfy the complementary principle, EMHA removes the one-to-one mapping constraint among queries and keys in multiple subspaces and allows each query to attend to multiple keys. On top of that, we develop a method to fully encourage consensus among heads by introducing two interaction models, namely inner-subspace interaction and cross-subspace interaction. Extensive experiments on a wide range of language tasks (e.g., machine translation, abstractive summarization and grammar correction, language modeling), show its superiority, with a very modest increase in model size. Our code would be available at: https://github.com/zhengkid/EIT-Enhanced-Interactive-Transformer.

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RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners
Chi Hu | Yuan Ge | Xiangnan Ma | Hang Cao | Qiang Li | Yonghua Yang | Tong Xiao | Jingbo Zhu
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Large Language Models (LLMs) have achieved impressive performance across various reasoning tasks. However, even state-of-the-art LLMs such as ChatGPT are prone to logical errors during their reasoning processes. Existing solutions, such as deploying task-specific verifiers or voting over multiple reasoning paths, either require extensive human annotations or fail in scenarios with inconsistent responses. To address these challenges, we introduce RankPrompt, a new prompting method that enables LLMs to self-rank their responses without additional resources. RankPrompt breaks down the ranking problem into a series of comparisons among diverse responses, leveraging the inherent capabilities of LLMs to generate chains of comparison as contextual exemplars. Our experiments across 11 arithmetic and commonsense reasoning tasks show that RankPrompt significantly enhances the reasoning performance of ChatGPT and GPT-4, with improvements of up to 13%. Moreover, RankPrompt excels in LLM-based automatic evaluations for open-ended tasks, aligning with human judgments 74% of the time in the AlpacaEval dataset. It also exhibits robustness to variations in response order and consistency. Collectively, our results validate RankPrompt as an effective method for eliciting high-quality feedback from language models.

2023

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CTC-based Non-autoregressive Speech Translation
Chen Xu | Xiaoqian Liu | Xiaowen Liu | Qingxuan Sun | Yuhao Zhang | Murun Yang | Qianqian Dong | Tom Ko | Mingxuan Wang | Tong Xiao | Anxiang Ma | Jingbo Zhu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency. In this paper, we investigate the potential of connectionist temporal classification (CTC) for non-autoregressive speech translation (NAST).In particular, we develop a model consisting of two encoders that are guided by CTC to predict the source and target texts, respectively. Introducing CTC into NAST on both language sides has obvious challenges: 1) the conditional independent generation somewhat breaks the interdependency among tokens, and 2) the monotonic alignment assumption in standard CTC does not hold in translation tasks. In response, we develop a prediction-aware encoding approach and a cross-layer attention approach to address these issues. We also use curriculum learning to improve convergence of training. Experiments on the MuST-C ST benchmarks show that our NAST model achieves an average BLEU score of 29.5 with a speed-up of 5.67×, which is comparable to the autoregressive counterpart and even outperforms the previous best result of 0.9 BLEU points.

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Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation
Yuchen Han | Chen Xu | Tong Xiao | Jingbo Zhu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Pre-training and fine-tuning is a paradigm for alleviating the data scarcity problem in end-to-end speech translation (E2E ST). The commonplace ”modality gap” between speech and text data often leads to inconsistent inputs between pre-training and fine-tuning. However, we observe that this gap occurs in the early stages of fine-tuning, but does not have a major impact on the final performance. On the other hand, we find that there has another gap, which we call the ”capacity gap”: high resource tasks (such as ASR and MT) always require a large model to fit, when the model is reused for a low resource task (E2E ST), it will get a sub-optimal performance due to the over-fitting. In a case study, we find that the regularization plays a more important role than the well-designed modality adaption method, which achieves 29.0 for en-de and 40.3 for en-fr on the MuST-C dataset.

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MobileNMT: Enabling Translation in 15MB and 30ms
Ye Lin | Xiaohui Wang | Zhexi Zhang | Mingxuan Wang | Tong Xiao | Jingbo Zhu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)

Deploying NMT models on mobile devices is essential for privacy, low latency, and offline scenarios. For high model capacity, NMT models are rather large. Running these models on devices is challenging with limited storage, memory, computation, and power consumption. Existing work either only focuses on a single metric such as FLOPs or general engine which is not good at auto-regressive decoding. In this paper, we present MobileNMT, a system that can translate in 15MB and 30ms on devices. We propose a series of principles for model compression when combined with quantization. Further, we implement an engine that is friendly to INT8 and decoding. With the co-design of model and engine, compared with the existing system, we speed up 47.0x and save 99.5% of memory with only 11.6% loss of BLEU. Our code will be publicly available after the anonymity period.

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The NiuTrans End-to-End Speech Translation System for IWSLT23 English-to-Chinese Offline Task
Yuchen Han | Xiaoqian Liu | Hao Chen | Yuhao Zhang | Chen Xu | Tong Xiao | Jingbo Zhu
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

This paper describes the NiuTrans end-to-end speech translation system submitted for the IWSLT 2023 English-to-Chinese offline task. Our speech translation models are composed of pre-trained ASR and MT models under the SATE framework. Several pre-trained models with diverse architectures and input representations (e.g., log Mel-filterbank and waveform) were utilized. We proposed an IDA method to iteratively improve the performance of the MT models and generate the pseudo ST data through MT systems. We then trained ST models with different structures and data settings to enhance ensemble performance. Experimental results demonstrate that our NiuTrans system achieved a BLEU score of 29.22 on the MuST-C En-Zh tst-COMMON set, outperforming the previous year’s submission by 0.12 BLEU despite using less MT training data.

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TranSFormer: Slow-Fast Transformer for Machine Translation
Bei Li | Yi Jing | Xu Tan | Zhen Xing | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: ACL 2023

Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems. Prior research has primarily focused on treating subwords as basic units in developing such systems. However, the incorporation of fine-grained character-level features into multiscale Transformer has not yet been explored. In this work, we present a Slow-Fast two-stream learning model, referred to as TranSFormer, which utilizes a “slow” branch to deal with subword sequences and a “fast” branch to deal with longer character sequences. This model is efficient since the fast branch is very lightweight by reducing the model width, and yet provides useful fine-grained features for the slow branch. Our TranSFormer shows consistent BLEU improvements (larger than 1 BLEU point) on several machine translation benchmarks.

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Augmenting Large Language Model Translators via Translation Memories
Yongyu Mu | Abudurexiti Reheman | Zhiquan Cao | Yuchun Fan | Bei Li | Yinqiao Li | Tong Xiao | Chunliang Zhang | Jingbo Zhu
Findings of the Association for Computational Linguistics: ACL 2023

Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to “understand” prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.

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Bridging the Granularity Gap for Acoustic Modeling
Chen Xu | Yuhao Zhang | Chengbo Jiao | Xiaoqian Liu | Chi Hu | Xin Zeng | Tong Xiao | Anxiang Ma | Huizhen Wang | Jingbo Zhu
Findings of the Association for Computational Linguistics: ACL 2023

While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies and distributing the attention weights. We propose Progressive Down-Sampling (PDS) which gradually compresses the acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. In addition, we develop a representation fusion method to alleviate information loss that occurs inevitably during high compression. In this way, we compress the acoustic features into 1/32 of the initial length while achieving better or comparable performances on the speech recognition task. And as a bonus, it yields inference speedups ranging from 1.20x to 1.47x.By reducing the modeling burden, we also achieve competitive results when training on the more challenging speech translation task.

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Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models
Hang Cao | Zhiquan Cao | Chi Hu | Baoyu Hou | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: ACL 2023

Grammatical Error Correction (GEC) aims to correct grammatical errors in sentences. We find that autoregressive models tend to assign low probabilities to tokens that need corrections. Here we introduce additional signals to the training of GEC models so that these systems can learn to better predict at ambiguous positions. To do this, we use a non-autoregressive model as an auxiliary model, and develop a new regularization term of training by considering the difference in predictions between the autoregressive and non-autoregressive models. We experiment with this method on both English and Chinese GEC tasks. Experimental results show that our GEC system outperforms the baselines on all the data sets significantly.

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Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
Yuxin Zuo | Bei Li | Chuanhao Lv | Tong Zheng | Tong Xiao | JingBo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2023

This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete. Instead, we attribute this phenomenon to insufficient cross-modal interaction, rather than image information redundancy. A novel approach is proposed to generate parallel Visual Question-Answering (VQA) style pairs from the source text, fostering more robust cross-modal interaction. Using Large Language Models (LLMs), we explicitly model the probing signal in MMT to convert it into VQA-style data to create the Multi30K-VQA dataset. An MMT-VQA multitask learning framework is introduced to incorporate explicit probing signals from the dataset into the MMT training process. Experimental results on two widely-used benchmarks demonstrate the effectiveness of this novel approach. Our code and data would be available at: https://github.com/libeineu/MMT-VQA.

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Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Yuhao Zhang | Chen Xu | Bei Li | Hao Chen | Tong Xiao | Chunliang Zhang | Jingbo Zhu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning. However, the extent to which auxiliary tasks are highly consistent with the ST task, and how much this approach truly helps, have not been thoroughly studied. In this paper, we investigate the consistency between different tasks, considering different times and modules. We find that the textual encoder primarily facilitates cross-modal conversion, but the presence of noise in speech impedes the consistency between text and speech representations. Furthermore, we propose an improved multi-task learning (IMTL) approach for the ST task, which bridges the modal gap by mitigating the difference in length and representation. We conduct experiments on the MuST-C dataset. The results demonstrate that our method attains state-of-the-art results. Moreover, when additional data is used, we achieve the new SOTA result on MuST-C English to Spanish task with 20.8% of the training time required by the current SOTA method.

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基于多尺度建模的端到端自动语音识别方法(An End-to-End Automatic Speech Recognition Method Based on Multiscale Modeling)
Hao Chen (陈昊) | Runlai Zhang (张润来) | Yuhao Zhang (张裕浩) | Chenghao Gao (高成浩) | Chen Xu (许晨) | Anxiang Ma (马安香) | Tong Xiao (肖桐) | Jingbo Zhu (朱靖波)
Proceedings of the 22nd Chinese National Conference on Computational Linguistics

“近年来,基于深度学习的端到端自动语音识别模型直接对语音和文本进行建模,结构简单且性能上也具有显著优势,逐渐成为主流。然而,由于连续的语音信号与离散的文本在长度及表示尺度上存在巨大差异,二者间的模态鸿沟问题是该类任务一直存在的困扰。为解决该问题,本文提出了多尺度语音识别建模方法,该方法从利用细粒度分布知识的角度出发,建立多个不同尺度形式的文本信息,将特征序列从细粒度的低层次序列逐步对齐预测出文本序列。这种逐级预测的方式能够有效降低预测难度,缓解模态鸿沟带来的影响,并通过融合不同尺度下特征,提高语料信息的丰富性与完整性,进一步增强模型推理能力。本文在LibriSpeech小规模、大规模和TEDLIUM2数据集上实验,相比基线系统词错误率平均降低1.7、0.45和0.76,验证了方法的有效性。”

2022

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On Vision Features in Multimodal Machine Translation
Bei Li | Chuanhao Lv | Zefan Zhou | Tao Zhou | Tong Xiao | Anxiang Ma | JingBo Zhu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision models. In this work, we investigate the impact of vision models on MMT. Given the fact that Transformer is becoming popular in computer vision, we experiment with various strong models (such as Vision Transformer) and enhanced features (such as object-detection and image captioning). We develop a selective attention model to study the patch-level contribution of an image in MMT. On detailed probing tasks, we find that stronger vision models are helpful for learning translation from the visual modality. Our results also suggest the need of carefully examining MMT models, especially when current benchmarks are small-scale and biased.

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ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation
Bei Li | Quan Du | Tao Zhou | Yi Jing | Shuhan Zhou | Xin Zeng | Tong Xiao | JingBo Zhu | Xuebo Liu | Min Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE methods. We first show that a residual block of layers in Transformer can be described as a higher-order solution to ODE. Inspired by this, we design a new architecture, ODE Transformer, which is analogous to the Runge-Kutta method that is well motivated in ODE. As a natural extension to Transformer, ODE Transformer is easy to implement and efficient to use. Experimental results on the large-scale machine translation, abstractive summarization, and grammar error correction tasks demonstrate the high genericity of ODE Transformer. It can gain large improvements in model performance over strong baselines (e.g., 30.77 and 44.11 BLEU scores on the WMT’14 English-German and English-French benchmarks) at a slight cost in inference efficiency.

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Multi-Path Transformer is Better: A Case Study on Neural Machine Translation
Ye Lin | Shuhan Zhou | Yanyang Li | Anxiang Ma | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2022

For years the model performance in machine learning obeyed a power-law relationship with the model size. For the consideration of parameter efficiency, recent studies focus on increasing model depth rather than width to achieve better performance. In this paper, we study how model width affects the Transformer model through a parameter-efficient multi-path structure. To better fuse features extracted from different paths, we add three additional operations to each sublayer: a normalization at the end of each path, a cheap operation to produce more features, and a learnable weighted mechanism to fuse all features flexibly. Extensive experiments on 12 WMT machine translation tasks show that, with the same number of parameters, the shallower multi-path model can achieve similar or even better performance than the deeper model. It reveals that we should pay more attention to the multi-path structure, and there should be a balance between the model depth and width to train a better large-scale Transformer.

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Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection
Chenglong Wang | Yi Lu | Yongyu Mu | Yimin Hu | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2022

Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model.In this process, we typically have multiple types of knowledge extracted from the teacher model.The problem is to make full use of them to train the student model.Our preliminary study shows that: (1) not all of the knowledge is necessary for learning a good student model, and (2) knowledge distillation can benefit from certain knowledge at different training steps.In response to these, we propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation.In addition, we offer a refinement of the training algorithm to ease the computational burden.Experimental results on the GLUE datasets show that our method outperforms several strong knowledge distillation baselines significantly.

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The NiuTrans Machine Translation Systems for WMT22
Weiqiao Shan | Zhiquan Cao | Yuchen Han | Siming Wu | Yimin Hu | Jie Wang | Yi Zhang | Hou Baoyu | Hang Cao | Chenghao Gao | Xiaowen Liu | Tong Xiao | Anxiang Ma | Jingbo Zhu
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper describes the NiuTrans neural machine translation systems of the WMT22 General MT constrained task. We participate in four directions, including Chinese→English, English→Croatian, and Livonian↔English. Our models are based on several advanced Transformer variants, e.g., Transformer-ODE, Universal Multiscale Transformer (UMST). The main workflow consists of data filtering, large-scale data augmentation (i.e., iterative back-translation, iterative knowledge distillation), and specific-domain fine-tuning. Moreover, we try several multi-domain methods, such as a multi-domain model structure and a multi-domain data clustering method, to rise to this year’s newly proposed multi-domain test set challenge. For low-resource scenarios, we build a multi-language translation model to enhance the performance, and try to use the pre-trained language model (mBERT) to initialize the translation model.

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The NiuTrans’s Submission to the IWSLT22 English-to-Chinese Offline Speech Translation Task
Yuhao Zhang | Canan Huang | Chen Xu | Xiaoqian Liu | Bei Li | Anxiang Ma | Tong Xiao | Jingbo Zhu
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This paper describes NiuTrans’s submission to the IWSLT22 English-to-Chinese (En-Zh) offline speech translation task. The end-to-end and bilingual system is built by constrained English and Chinese data and translates the English speech to Chinese text without intermediate transcription. Our speech translation models are composed of different pre-trained acoustic models and machine translation models by two kinds of adapters. We compared the effect of the standard speech feature (e.g. log Mel-filterbank) and the pre-training speech feature and try to make them interact. The final submission is an ensemble of three potential speech translation models. Our single best and ensemble model achieves 18.66 BLEU and 19.35 BLEU separately on MuST-C En-Zh tst-COMMON set.

2021

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Weight Distillation: Transferring the Knowledge in Neural Network Parameters
Ye Lin | Yanyang Li | Ziyang Wang | Bei Li | Quan Du | Tong Xiao | Jingbo Zhu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Knowledge distillation has been proven to be effective in model acceleration and compression. It transfers knowledge from a large neural network to a small one by using the large neural network predictions as targets of the small neural network. But this way ignores the knowledge inside the large neural networks, e.g., parameters. Our preliminary study as well as the recent success in pre-training suggests that transferring parameters are more effective in distilling knowledge. In this paper, we propose Weight Distillation to transfer the knowledge in parameters of a large neural network to a small neural network through a parameter generator. On the WMT16 En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks, our experiments show that weight distillation learns a small network that is 1.88 2.94x faster than the large network but with competitive BLEU performance. When fixing the size of small networks, weight distillation outperforms knowledge distillation by 0.51 1.82 BLEU points.

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Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders
Chen Xu | Bojie Hu | Yanyang Li | Yuhao Zhang | Shen Huang | Qi Ju | Tong Xiao | Jingbo Zhu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for translation, whereas MT encoders are not designed to deal with long but locally attentive acoustic sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. Our encoder begins with processing the acoustic sequence as usual, but later behaves more like an MT encoder for a global representation of the input sequence. In this way, it is straightforward to incorporate the pre-trained models into the system. Also, we develop an adaptor module to alleviate the representation inconsistency between the pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge distillation method to preserve the pre-training knowledge. Experimental results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.

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The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task
Chen Xu | Xiaoqian Liu | Xiaowen Liu | Tiger Wang | Canan Huang | Tong Xiao | Jingbo Zhu
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription. We use the Transformer-based model architecture and enhance it by Conformer, relative position encoding, and stacked acoustic and textual encoding. To augment the training data, the English transcriptions are translated to German translations. Finally, we employ ensemble decoding to integrate the predictions from several models trained with the different datasets. Combining these techniques, we achieve 33.84 BLEU points on the MuST-C En-De test set, which shows the enormous potential of the end-to-end model.

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利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)
Gechao Wang (王屹超) | Muhua Zhu (朱慕华) | Chen Xu (许晨) | Yan Zhang (张琰) | Huizhen Wang (王会珍) | Jingbo Zhu (朱靖波)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

视觉问答作为多模态任务,需要深度理解图像和文本问题从而推理出答案。然而在许多情况下,仅在图像和问题上进行简单推理难以得到正确的答案,事实上还有其它有效的信息可以被利用,例如图像描述、外部知识等。针对以上问题,本文提出了利用图像描述和外部知识增强表示的视觉问答模型。该模型以问题为导向,基于协同注意力机制分别在图像和其描述上进行编码,并且利用知识图谱嵌入,将外部知识编码到模型当中,丰富了模型的特征表示,增强模型的推理能力。在OKVQA数据集上的实验结果表明本文方法相比基线系统有1.71%的准确率提升,与先前工作中的主流模型相比也有1.88%的准确率提升,证明了本文方法的有效性。

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The NiuTrans Machine Translation Systems for WMT21
Shuhan Zhou | Tao Zhou | Binghao Wei | Yingfeng Luo | Yongyu Mu | Zefan Zhou | Chenglong Wang | Xuanjun Zhou | Chuanhao Lv | Yi Jing | Laohu Wang | Jingnan Zhang | Canan Huang | Zhongxiang Yan | Chi Hu | Bei Li | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary systems are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.

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The NiuTrans System for the WMT 2021 Efficiency Task
Chenglong Wang | Chi Hu | Yongyu Mu | Zhongxiang Yan | Siming Wu | Yimin Hu | Hang Cao | Bei Li | Ye Lin | Tong Xiao | Jingbo Zhu
Proceedings of the Sixth Conference on Machine Translation

This paper describes the NiuTrans system for the WMT21 translation efficiency task. Following last year’s work, we explore various techniques to improve the efficiency while maintaining translation quality. We investigate the combinations of lightweight Transformer architectures and knowledge distillation strategies. Also, we improve the translation efficiency with graph optimization, low precision, dynamic batching, and parallel pre/post-processing. Putting these together, our system can translate 247,000 words per second on an NVIDIA A100, being 3× faster than our last year’s system. Our system is the fastest and has the lowest memory consumption on the GPU-throughput track. The code, model, and pipeline will be available at NiuTrans.NMT.

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Bag of Tricks for Optimizing Transformer Efficiency
Ye Lin | Yanyang Li | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2021

Improving Transformer efficiency has become increasingly attractive recently. A wide range of methods has been proposed, e.g., pruning, quantization, new architectures and etc. But these methods are either sophisticated in implementation or dependent on hardware. In this paper, we show that the efficiency of Transformer can be improved by combining some simple and hardware-agnostic methods, including tuning hyper-parameters, better design choices and training strategies. On the WMT news translation tasks, we improve the inference efficiency of a strong Transformer system by 3.80x on CPU and 2.52x on GPU.

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RankNAS: Efficient Neural Architecture Search by Pairwise Ranking
Chi Hu | Chenglong Wang | Xiangnan Ma | Xia Meng | Yinqiao Li | Tong Xiao | Jingbo Zhu | Changliang Li
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem. Previous methods require numerous training examples to estimate the accurate performance of architectures, although the actual goal is to find the distinction between “good” and “bad” candidates. Here we do not resort to performance predictors. Instead, we propose a performance ranking method (RankNAS) via pairwise ranking. It enables efficient architecture search using much fewer training examples. Moreover, we develop an architecture selection method to prune the search space and concentrate on more promising candidates. Extensive experiments on machine translation and language modeling tasks show that RankNAS can design high-performance architectures while being orders of magnitude faster than state-of-the-art NAS systems.

2020

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Dynamic Curriculum Learning for Low-Resource Neural Machine Translation
Chen Xu | Bojie Hu | Yufan Jiang | Kai Feng | Zeyang Wang | Shen Huang | Qi Ju | Tong Xiao | Jingbo Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.

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Layer-Wise Multi-View Learning for Neural Machine Translation
Qiang Wang | Changliang Li | Yue Zhang | Tong Xiao | Jingbo Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Traditional neural machine translation is limited to the topmost encoder layer’s context representation and cannot directly perceive the lower encoder layers. Existing solutions usually rely on the adjustment of network architecture, making the calculation more complicated or introducing additional structural restrictions. In this work, we propose layer-wise multi-view learning to solve this problem, circumventing the necessity to change the model structure. We regard each encoder layer’s off-the-shelf output, a by-product in layer-by-layer encoding, as the redundant view for the input sentence. In this way, in addition to the topmost encoder layer (referred to as the primary view), we also incorporate an intermediate encoder layer as the auxiliary view. We feed the two views to a partially shared decoder to maintain independent prediction. Consistency regularization based on KL divergence is used to encourage the two views to learn from each other. Extensive experimental results on five translation tasks show that our approach yields stable improvements over multiple strong baselines. As another bonus, our method is agnostic to network architectures and can maintain the same inference speed as the original model.

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A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction
Yanyang Li | Yingfeng Luo | Ye Lin | Quan Du | Huizhen Wang | Shujian Huang | Tong Xiao | Jingbo Zhu
Proceedings of the 28th International Conference on Computational Linguistics

Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese. In this work, we show that this failure results from the gap between the actual initialization performance and the minimum initialization performance for the self-learning to succeed. We propose Iterative Dimension Reduction to bridge this gap. Our experiments show that this simple method does not hamper the performance of similar language pairs and achieves an accuracy of 13.64 55.53% between English and four distant languages, i.e., Chinese, Japanese, Vietnamese and Thai.

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The NiuTrans System for WNGT 2020 Efficiency Task
Chi Hu | Bei Li | Yinqiao Li | Ye Lin | Yanyang Li | Chenglong Wang | Tong Xiao | Jingbo Zhu
Proceedings of the Fourth Workshop on Neural Generation and Translation

This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task. We focus on the efficient implementation of deep Transformer models (Wang et al., 2019; Li et al., 2019) using NiuTensor, a flexible toolkit for NLP tasks. We explored the combination of deep encoder and shallow decoder in Transformer models via model compression and knowledge distillation. The neural machine translation decoding also benefits from FP16 inference, attention caching, dynamic batching, and batch pruning. Our systems achieve promising results in both translation quality and efficiency, e.g., our fastest system can translate more than 40,000 tokens per second with an RTX 2080 Ti while maintaining 42.9 BLEU on newstest2018.

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The NiuTrans Machine Translation Systems for WMT20
Yuhao Zhang | Ziyang Wang | Runzhe Cao | Binghao Wei | Weiqiao Shan | Shuhan Zhou | Abudurexiti Reheman | Tao Zhou | Xin Zeng | Laohu Wang | Yongyu Mu | Jingnan Zhang | Xiaoqian Liu | Xuanjun Zhou | Yinqiao Li | Bei Li | Tong Xiao | Jingbo Zhu
Proceedings of the Fifth Conference on Machine Translation

This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese<->English, English->Chinese, Inuktitut->English and Tamil->English total five tasks and rank first in Japanese<->English both sides. We mainly utilized iterative back-translation, different depth and widen model architectures, iterative knowledge distillation and iterative fine-tuning. And we find that adequately widened and deepened the model simultaneously, the performance will significantly improve. Also, iterative fine-tuning strategy we implemented is effective during adapting domain. For Inuktitut->English and Tamil->English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.

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The NiuTrans System for the WMT20 Quality Estimation Shared Task
Chi Hu | Hui Liu | Kai Feng | Chen Xu | Nuo Xu | Zefan Zhou | Shiqin Yan | Yingfeng Luo | Chenglong Wang | Xia Meng | Tong Xiao | Jingbo Zhu
Proceedings of the Fifth Conference on Machine Translation

This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task. We participated in all tasks and all language pairs. We explored the combination of transfer learning, multi-task learning and model ensemble. Results on multiple tasks show that deep transformer machine translation models and multilingual pretraining methods significantly improve translation quality estimation performance. Our system achieved remarkable results in multiple level tasks, e.g., our submissions obtained the best results on all tracks in the sentence-level Direct Assessment task.

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Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
Bei Li | Hui Liu | Ziyang Wang | Yufan Jiang | Tong Xiao | Jingbo Zhu | Tongran Liu | Changliang Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in document-level neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.

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Learning Architectures from an Extended Search Space for Language Modeling
Yinqiao Li | Chi Hu | Yuhao Zhang | Nuo Xu | Yufan Jiang | Tong Xiao | Jingbo Zhu | Tongran Liu | Changliang Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. In this paper, we extend the search space of NAS. In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS). For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously. We implement our model in a differentiable architecture search system. For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and WikiText data, with a new state-of-the-art on PTB. Moreover, the learned architectures show good transferability to other systems. E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale pre-learned architectures.

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Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation
Qiang Wang | Tong Xiao | Jingbo Zhu
Findings of the Association for Computational Linguistics: EMNLP 2020

The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly. Such individual training leads to increased model maintenance costs and slower model iterations, especially for the industry. In this work, we propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference. Experimental results show that our approach can simultaneously support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method——LayerDrop.

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Shallow-to-Deep Training for Neural Machine Translation
Bei Li | Ziyang Wang | Hui Liu | Yufan Jiang | Quan Du | Tong Xiao | Huizhen Wang | Jingbo Zhu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but training an extremely deep encoder is time consuming. Moreover, why deep models help NMT is an open question. In this paper, we investigate the behavior of a well-tuned deep Transformer system. We find that stacking layers is helpful in improving the representation ability of NMT models and adjacent layers perform similarly. This inspires us to develop a shallow-to-deep training method that learns deep models by stacking shallow models. In this way, we successfully train a Transformer system with a 54-layer encoder. Experimental results on WMT’16 English-German and WMT’14 English-French translation tasks show that it is 1:4 faster than training from scratch, and achieves a BLEU score of 30:33 and 43:29 on two tasks. The code is publicly available at https://github.com/libeineu/SDT-Training.

2019

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Learning Deep Transformer Models for Machine Translation
Qiang Wang | Bei Li | Tong Xiao | Jingbo Zhu | Changliang Li | Derek F. Wong | Lidia S. Chao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Transformer is the state-of-the-art model in recent machine translation evaluations. Two strands of research are promising to improve models of this kind: the first uses wide networks (a.k.a. Transformer-Big) and has been the de facto standard for development of the Transformer system, and the other uses deeper language representation but faces the difficulty arising from learning deep networks. Here, we continue the line of research on the latter. We claim that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next. On WMT’16 English-German and NIST OpenMT’12 Chinese-English tasks, our deep system (30/25-layer encoder) outperforms the shallow Transformer-Big/Base baseline (6-layer encoder) by 0.4-2.4 BLEU points. As another bonus, the deep model is 1.6X smaller in size and 3X faster in training than Transformer-Big.

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Shared-Private Bilingual Word Embeddings for Neural Machine Translation
Xuebo Liu | Derek F. Wong | Yang Liu | Lidia S. Chao | Tong Xiao | Jingbo Zhu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word embedding is central to neural machine translation (NMT), which has attracted intensive research interest in recent years. In NMT, the source embedding plays the role of the entrance while the target embedding acts as the terminal. These layers occupy most of the model parameters for representation learning. Furthermore, they indirectly interface via a soft-attention mechanism, which makes them comparatively isolated. In this paper, we propose shared-private bilingual word embeddings, which give a closer relationship between the source and target embeddings, and which also reduce the number of model parameters. For similar source and target words, their embeddings tend to share a part of the features and they cooperatively learn these common representation units. Experiments on 5 language pairs belonging to 6 different language families and written in 5 different alphabets demonstrate that the proposed model provides a significant performance boost over the strong baselines with dramatically fewer model parameters.

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Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
Yufan Jiang | Chi Hu | Tong Xiao | Chunliang Zhang | Jingbo Zhu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing. In particular, we improve differentiable architecture search by removing the softmax-local constraint. Also, we apply differentiable NAS to named entity recognition (NER). It is the first time that differentiable NAS methods are adopted in NLP tasks other than language modeling. On both the PTB language modeling and CoNLL-2003 English NER data, our method outperforms strong baselines. It achieves a new state-of-the-art on the NER task.

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The NiuTrans Machine Translation Systems for WMT19
Bei Li | Yinqiao Li | Chen Xu | Ye Lin | Jiqiang Liu | Hui Liu | Ziyang Wang | Yuhao Zhang | Nuo Xu | Zeyang Wang | Kai Feng | Hexuan Chen | Tengbo Liu | Yanyang Li | Qiang Wang | Tong Xiao | Jingbo Zhu
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {KK↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.

2018

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Detecting Free Translation in Parallel Corpora from Attention Scores
Qi Chen | Oi Yee Kwong | Jingbo Zhu
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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A Simple and Effective Approach to Coverage-Aware Neural Machine Translation
Yanyang Li | Tong Xiao | Yinqiao Li | Qiang Wang | Changming Xu | Jingbo Zhu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT). Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs. Moreover, it is robust to large beam sizes, which is not well studied in previous work. On the Chinese-English and English-German translation tasks, our approach yields +0.4 1.5 BLEU improvements over the state-of-the-art baselines.

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Multi-layer Representation Fusion for Neural Machine Translation
Qiang Wang | Fuxue Li | Tong Xiao | Yanyang Li | Yinqiao Li | Jingbo Zhu
Proceedings of the 27th International Conference on Computational Linguistics

Neural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level representations. This makes it more difficult to train the model and poses a risk of information loss to prediction. In this paper, we propose a multi-layer representation fusion (MLRF) approach to fusing stacked layers. In particular, we design three fusion functions to learn a better representation from the stack. Experimental results show that our approach yields improvements of 0.92 and 0.56 BLEU points over the strong Transformer baseline on IWSLT German-English and NIST Chinese-English MT tasks respectively. The result is new state-of-the-art in German-English translation.

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The NiuTrans Machine Translation System for WMT18
Qiang Wang | Bei Li | Jiqiang Liu | Bojian Jiang | Zheyang Zhang | Yinqiao Li | Ye Lin | Tong Xiao | Jingbo Zhu
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks. Our baseline systems are based on the Transformer architecture. We further improve the translation performance 2.4-2.6 BLEU points from four aspects, including architectural improvements, diverse ensemble decoding, reranking, and post-processing. Among constrained submissions, we rank 2nd out of 16 submitted systems on Chinese → English task and 3rd out of 16 on English → Chinese task, respectively.

2017

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Towards Bidirectional Hierarchical Representations for Attention-based Neural Machine Translation
Baosong Yang | Derek F. Wong | Tong Xiao | Lidia S. Chao | Jingbo Zhu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper proposes a hierarchical attentional neural translation model which focuses on enhancing source-side hierarchical representations by covering both local and global semantic information using a bidirectional tree-based encoder. To maximize the predictive likelihood of target words, a weighted variant of an attention mechanism is used to balance the attentive information between lexical and phrase vectors. Using a tree-based rare word encoding, the proposed model is extended to sub-word level to alleviate the out-of-vocabulary (OOV) problem. Empirical results reveal that the proposed model significantly outperforms sequence-to-sequence attention-based and tree-based neural translation models in English-Chinese translation tasks.

2015

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NiuParser: A Chinese Syntactic and Semantic Parsing Toolkit
Jingbo Zhu | Muhua Zhu | Qiang Wang | Tong Xiao
Proceedings of ACL-IJCNLP 2015 System Demonstrations

2014

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Syntactic SMT Using a Discriminative Text Generation Model
Yue Zhang | Kai Song | Linfeng Song | Jingbo Zhu | Qun Liu
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Tagging The Web: Building A Robust Web Tagger with Neural Network
Ji Ma | Yue Zhang | Jingbo Zhu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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A Hybrid Approach to Skeleton-based Translation
Tong Xiao | Jingbo Zhu | Chunliang Zhang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Punctuation Processing for Projective Dependency Parsing
Ji Ma | Yue Zhang | Jingbo Zhu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Kalina Bontcheva | Jingbo Zhu
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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Effective Incorporation of Source Syntax into Hierarchical Phrase-based Translation
Tong Xiao | Adrià de Gispert | Jingbo Zhu | Bill Byrne
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Fast and Accurate Shift-Reduce Constituent Parsing
Muhua Zhu | Yue Zhang | Wenliang Chen | Min Zhang | Jingbo Zhu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Easy-First POS Tagging and Dependency Parsing with Beam Search
Ji Ma | Jingbo Zhu | Tong Xiao | Nan Yang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing
Liang-Chih Yu | Yuen-Hsien Tseng | Jingbo Zhu | Fuji Ren
Proceedings of the Seventh SIGHAN Workshop on Chinese Language Processing

2012

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Learning Better Rule Extraction with Translation Span Alignment
Jingbo Zhu | Tong Xiao | Chunliang Zhang
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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NiuTrans: An Open Source Toolkit for Phrase-based and Syntax-based Machine Translation
Tong Xiao | Jingbo Zhu | Hao Zhang | Qiang Li
Proceedings of the ACL 2012 System Demonstrations

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NEU Systems in SIGHAN Bakeoff 2012
Ji Ma | LongFei Bai | Zhuo Liu | Ao Zhang | Jingbo Zhu
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Easy-First Chinese POS Tagging and Dependency Parsing
Ji Ma | Tong Xiao | Jingbo Zhu | Feiliang Ren
Proceedings of COLING 2012

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Exploiting Lexical Dependencies from Large-Scale Data for Better Shift-Reduce Constituency Parsing
Muhua Zhu | Jingbo Zhu | Huizhen Wang
Proceedings of COLING 2012

2011

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Improving Decoding Generalization for Tree-to-String Translation
Jingbo Zhu | Tong Xiao
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Better Automatic Treebank Conversion Using A Feature-Based Approach
Muhua Zhu | Jingbo Zhu | Minghan Hu
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Document-level Consistency Verification in Machine Translation
Tong Xiao | Jingbo Zhu | Shujie Yao | Hao Zhang
Proceedings of Machine Translation Summit XIII: Papers

2010

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Boosting-Based System Combination for Machine Translation
Tong Xiao | Jingbo Zhu | Muhua Zhu | Huizhen Wang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Mining Large-scale Parallel Corpora from Multilingual Patents: An English-Chinese example and its application to SMT
Bin Lu | Benjamin K. Tsou | Tao Jiang | Oi Yee Kwong | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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High OOV-Recall Chinese Word Segmenter
Xiaoming Xu | Muhua Zhu | Xiaoxu Fei | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Chinese Syntactic Parsing Evaluation
Qiang Zhou | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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A Multi-stage Clustering Framework for Chinese Personal Name Disambiguation
Huizhen Wang | Haibo Ding | Yingchao Shi | Ji Ma | Xiao Zhou | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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NEUNLPLab Chinese Word Sense Induction System for SIGHAN Bakeoff 2010
Hao Zhang | Tong Xiao | Jingbo Zhu
CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Heterogeneous Parsing via Collaborative Decoding
Muhua Zhu | Jingbo Zhu | Tong Xiao
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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An Empirical Study of Translation Rule Extraction with Multiple Parsers
Tong Xiao | Jingbo Zhu | Hao Zhang | Muhua Zhu
Coling 2010: Posters

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Automatic Treebank Conversion via Informed Decoding
Muhua Zhu | Jingbo Zhu
Coling 2010: Posters

2009

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Better Synchronous Binarization for Machine Translation
Tong Xiao | Mu Li | Dongdong Zhang | Jingbo Zhu | Ming Zhou
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Chinese-English Organization Name Translation Based on Correlative Expansion
Feiliang Ren | Muhua Zhu | Huizhen Wang | Jingbo Zhu
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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The Construction of a Chinese-English Patent Parallel Corpus
Bin Lu | Benjamin K. Tsou | Jingbo Zhu | Tao Jiang | Oi Yee Kwong
Proceedings of the Third Workshop on Patent Translation

2008

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Learning a Stopping Criterion for Active Learning for Word Sense Disambiguation and Text Classification
Jingbo Zhu | Huizhen Wang | Eduard Hovy
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Towards Automated Semantic Analysis on Biomedical Research Articles
Donghui Feng | Gully Burns | Jingbo Zhu | Eduard Hovy
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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An Effective Hybrid Machine Learning Approach for Coreference Resolution
Feiliang Ren | Jingbo Zhu
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

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Which Performs Better on In-Vocabulary Word Segmentation: Based on Word or Character?
Zhenxing Wang | Changning Huang | Jingbo Zhu
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

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The Character-based CRF Segmenter of MSRA&NEU for the 4th Bakeoff
Zhenxing Wang | Changning Huang | Jingbo Zhu
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing

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Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation
Jingbo Zhu | Huizhen Wang | Eduard Hovy
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification
Jingbo Zhu | Huizhen Wang | Tianshun Yao | Benjamin K Tsou
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem
Jingbo Zhu | Eduard Hovy
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Designing Special Post-Processing Rules for SVM-Based Chinese Word Segmentation
Muhua Zhu | Yilin Wang | Zhenxing Wang | Huizhen Wang | Jingbo Zhu
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

2005

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Using Multiple Discriminant Analysis Approach for Linear Text Segmentation
Jingbo Zhu | Na Ye | Xinzhi Chang | Wenliang Chen | Benjamin K Tsou
Second International Joint Conference on Natural Language Processing: Full Papers

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Some Studies on Chinese Domain Knowledge Dictionary and Its Application to Text Classification
Jingbo Zhu | Wenliang Chen
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing

2002

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A Knowledge-based Approach to Text Classification
Jingbo Zhu | Tianshun Yao
COLING-02: The First SIGHAN Workshop on Chinese Language Processing

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