Zhen Zhang


2023

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RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue
Zhengliang Shi | Weiwei Sun | Shuo Zhang | Zhen Zhang | Pengjie Ren | Zhaochun Ren
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now, automatic evaluation methods need better consistency with humans, while reliable human evaluation can be time- and cost-intensive. To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem. Specifically, RADE explicitly compares reference and the candidate response to predict their overall scores.Moreover, an auxiliary response generation task enhances prediction via a shared encoder.To support RADE, we extend three datasets with additional rated responses other than just a golden response by human annotation.Experiments on our three datasets and two existing benchmarks demonstrate the effectiveness of our method, where Pearson, Spearman, and Kendall correlations with human evaluation outperform state-of-the-art baselines.

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OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models
Shengding Hu | Ning Ding | Weilin Zhao | Xingtai Lv | Zhen Zhang | Zhiyuan Liu | Maosong Sun
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as “delta tuning” in Ding et al. (2022), which updates only a small subset of parameters, known as “delta modules”, while keeping the backbone model’s parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.

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E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Zhen Zhang | Mengting Hu | Shiwan Zhao | Minlie Huang | Haotian Wang | Lemao Liu | Zhirui Zhang | Zhe Liu | Bingzhe Wu
Findings of the Association for Computational Linguistics: ACL 2023

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.

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Lexical Translation Inconsistency-Aware Document-Level Translation Repair
Zhen Zhang | Junhui Li | Shimin Tao | Hao Yang
Findings of the Association for Computational Linguistics: ACL 2023

Following the idea of “one translation per discourse”, in this paper we aim to improve translation consistency via document-level translation repair (DocRepair), i.e., automatic post-editing on translations of documents. To this end, we propose a lexical translation inconsistency-aware DocRepair to explicitly model translation inconsistency. First we locate the inconsistency in automatic translation. Then we provide translation candidates for those inconsistency. Finally, we propose lattice-like input to properly model inconsistent tokens and phrases and their candidates. Experimental results on three document-level translation datasets show that based on G-Transformer, a state-of-the-art document-to-document (Doc2Doc) translation model, our Doc2Doc DocRepair achieves significant improvement on translation quality in BLEU scores, but also greatly improves lexical translation consistency.

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Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction
Mengting Hu | Yinhao Bai | Yike Wu | Zhen Zhang | Liqi Zhang | Hang Gao | Shiwan Zhao | Minlie Huang
Findings of the Association for Computational Linguistics: ACL 2023

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates.

2022

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HwTscSU’s Submissions on WAT 2022 Shared Task
Yilun Liu | Zhen Zhang | Shimin Tao | Junhui Li | Hao Yang
Proceedings of the 9th Workshop on Asian Translation

In this paper we describe our submission to the shared tasks of the 9th Workshop on Asian Translation (WAT 2022) on NICT–SAP under the team name ”HwTscSU”. The tasks involve translation from 5 languages into English and vice-versa in two domains: IT domain and Wikinews domain. The purpose is to determine the feasibility of multilingualism, domain adaptation or document-level knowledge given very little to none clean parallel corpora for training. Our approach for all translation tasks mainly focused on pre-training NMT models on general datasets and fine-tuning them on domain-specific datasets. Due to the small amount of parallel corpora, we collected and cleaned the OPUS dataset including three IT domain corpora, i.e., GNOME, KDE4, and Ubuntu. We then trained Transformer models on the collected dataset and fine-tuned on corresponding dev set. The BLEU scores greatly improved in comparison with other systems. Our submission ranked 1st in all IT-domain tasks and in one out of eight ALT domain tasks.

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PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting
Zhen Zhang | Wei Zhu | Jinfan Zhang | Peng Wang | Rize Jin | Tae-Sun Chung
Findings of the Association for Computational Linguistics: NAACL 2022

BERT and other pretrained language models (PLMs) are ubiquitous in modern NLP. Even though PLMs are the state-of-the-art (SOTA) models for almost every NLP task (CITATION), the significant latency during inference prohibits wider industrial usage. In this work, we propose Patient and Confident Early Exiting BERT (PCEE-BERT), an off-the-shelf sample-dependent early exiting method that can work with different PLMs and can also work along with popular model compression methods. With a multi-exit BERT as the backbone model, PCEE-BERT will make the early exiting decision if enough numbers (patience parameter) of consecutive intermediate layers are confident about their predictions. The entropy value measures the confidence level of an intermediate layer’s prediction. Experiments on the GLUE benchmark demonstrate that our method outperforms previous SOTA early exiting methods. Ablation studies show that: (a) our method performs consistently well on other PLMs, such as ALBERT and TinyBERT; (b) PCEE-BERT can achieve different speed-up ratios by adjusting the patience parameter and the confidence threshold. The code for PCEE-BERT can be found at https://github.com/michael-wzhu/PCEE-BERT.