Ziqian Zeng


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On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
Jianwei Wang | Tianyin Wang | Ziqian Zeng
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot classification methods converted the task to other NLP tasks (e.g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data. A potentially valuable by-product of these methods is the large-scale silver standard data, i.e., pseudo-labeled data by the off-the-shelf models of other NLP tasks. However, there is no further investigation into the use of these data. In this paper, we propose a new framework, Clean-LaVe, which aims to utilize silver standard data to enhance the zero-shot performance. Clean-LaVe includes four phases: (1) Obtaining silver data; (2) Identifying relatively clean data from silver data; (3) Finetuning the off-the-shelf model using clean data; (4) Inference on the test data. The experimental results show that Clean-LaVe can outperform the baseline by 5% and 6% on TACRED and Wiki80 dataset in the zero-shot relation classification task, and by 3% ~7 % on Smile (Korean and Polish) in the zero-shot cross-lingual relation classification task, and by 8% on ACE05-E+ in the zero-shot event argument classification task.

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Zero-shot Event Detection Using a Textual Entailment Model as an Enhanced Annotator
Ziqian Zeng | Runyu Wu | Yuxiang Xiao | Xiaoda Zhong | Hanlin Wang | Zhengdong Lu | Huiping Zhuang
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Zero-shot event detection is a challenging task. Recent research work proposed to use a pre-trained textual entailment (TE) model on this task. However, those methods treated the TE model as a frozen annotator. We treat the TE model as an annotator that can be enhanced. We propose to use TE models to annotate large-scale unlabeled text and use annotated data to finetune the TE model, yielding an improved TE model. Finally, the improved TE model is used for inference on the test set. To improve the efficiency, we propose to use keywords to filter out sentences with a low probability of expressing event(s). To improve the coverage of keywords, we expand limited number of seed keywords using WordNet, so that we can use the TE model to annotate unlabeled text efficiently. The experimental results show that our method can outperform other baselines by 15% on the ACE05 dataset.


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Adaptive Policy with Wait-k Model for Simultaneous Translation
Libo Zhao | Kai Fan | Wei Luo | Wu Jing | Shushu Wang | Ziqian Zeng | Zhongqiang Huang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-k policy coupled with a standalone wait-k translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-k model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-k model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.

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From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
Hongliang Dai | Ziqian Zeng
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

For the task of fine-grained entity typing (FET), due to the use of a large number of entity types, it is usually considered too costly to manually annotating a training dataset that contains an ample number of examples for each type. A common way to address this problem is to use distantly annotated training data that contains incorrect labels. However, the performance of models trained solely with such data can be limited by the errors in the automatic annotation. Recently, there are a few approaches that no longer follow this conventional way. But without using sufficient direct entity typing supervision may also cause them to yield inferior performance. In this paper, we propose a new approach that can avoid the need of creating distantly labeled data whenever there is a new type schema. We first train an entity typing model that have an extremely board type coverage by using the ultra-fine entity typing data. Then, when there is a need to produce a model for a newly designed fine-grained entity type schema. We can simply fine-tune the previously trained model with a small number of examples annotated under this schema. Experimental results show that our approach achieves outstanding performance for FET under the few-shot setting. It can also outperform state-of-the-art weak supervision based methods after fine-tuning the model with only a small size manually annotated training set.


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Weakly Supervised Text Classification using Supervision Signals from a Language Model
Ziqian Zeng | Weimin Ni | Tianqing Fang | Xiang Li | Xinran Zhao | Yangqiu Song
Findings of the Association for Computational Linguistics: NAACL 2022

Solving text classification in a weakly supervised manner is important for real-world applications where human annotations are scarce. In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals. We design a prompt which combines the document itself and “this article is talking about [MASK].” A masked language model can generate words for the [MASK] token. The generated words which summarize the content of a document can be utilized as supervision signals. We propose a latent variable model to learn a word distribution learner which associates generated words to pre-defined categories and a document classifier simultaneously without using any annotated data. Evaluation on three datasets, AGNews, 20Newsgroups, and UCINews, shows that our method can outperform baselines by 2%, 4%, and 3%.


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Variational Weakly Supervised Sentiment Analysis with Posterior Regularization
Ziqian Zeng | Yangqiu Song
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Sentiment analysis is an important task in natural language processing (NLP). Most of existing state-of-the-art methods are under the supervised learning paradigm. However, human annotations can be scarce. Thus, we should leverage more weak supervision for sentiment analysis. In this paper, we propose a posterior regularization framework for the variational approach to the weakly supervised sentiment analysis to better control the posterior distribution of the label assignment. The intuition behind the posterior regularization is that if extracted opinion words from two documents are semantically similar, the posterior distributions of two documents should be similar. Our experimental results show that the posterior regularization can improve the original variational approach to the weakly supervised sentiment analysis and the performance is more stable with smaller prediction variance.


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A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
Ziqian Zeng | Wenxuan Zhou | Xin Liu | Yangqiu Song
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as “supervision.” These word pairs can be extracted by using dependency parsers and simple rules. Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i.e., the sentiment polarity, to the objective function, we can inject the sentiment polarity classifier to the objective via the variational lower bound. We can learn a sentiment polarity classifier by optimizing the lower bound. We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect.