Haoran Zhang


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Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses
Aysa Fan | Haoran Zhang | Luc Paquette | Rui Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts for GPT4, guiding it to generate code-tracing questions based on code snippets and descriptions. We established a set of human evaluation metrics to assess the quality of questions produced by the model compared to those created by human experts. Our analysis provides insights into the capabilities and potential of LLMs in generating diverse code-tracing questions. Additionally, we present a unique dataset of human and LLM-generated tracing questions, serving as a valuable resource for both the education and NLP research communities. This work contributes to the ongoing dialogue on the potential uses of LLMs in educational settings.

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Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Sarkar Snigdha Sarathi Das | Haoran Zhang | Peng Shi | Wenpeng Yin | Rui Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Unified Sequence Labeling that articulates different sequence labeling problems such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized sequence-to-sequence format opens up the opportunity to make the maximum utilization of large language model knowledge toward structured prediction. Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format. This significantly bounds its usefulness in data-limited settings where finetuning large models cannot properly generalize to the target format. To address this challenge and leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples, during the fine-tuning process. By leveraging the dynamism of sparsity, our approach mitigates the impact of well-learned samples and prioritizes underperforming instances for improvement in generalization. Across five tasks of sequence labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low resource settings offering upto 40% performance improvements over full fine-tuning depending on target evaluation settings. Also, compared to in-context learning and other parameter-efficient fine-tuning approaches, FISH-DIP performs comparably or better, notably in extreme low-resource settings. The source code of FISH-DIP will be available at [this URL](https://github.com/psunlpgroup/FISH-DIP)


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Essay Quality Signals as Weak Supervision for Source-based Essay Scoring
Haoran Zhang | Diane Litman
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs.


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Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring
Haoran Zhang | Diane Litman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.

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Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation
Na Liu | Xiangdong Su | Haoran Zhang | Guanglai Gao | Feilong Bao
Proceedings of the 28th International Conference on Computational Linguistics

Mongolian morphological segmentation is regarded as a crucial preprocessing step in many Mongolian related NLP applications and has received extensive attention. Recently, end-to-end segmentation approaches with long short-term memory networks (LSTM) have achieved excellent results. However, the inner-word features among characters in the word and the out-word features from context are not well utilized in the segmentation process. In this paper, we propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation. The network consists of two encoders and one decoder. The inner-word encoder uses the self-attention mechanisms to capture the inner-word features of the target word. The out-word encoder employs a two layers BiLSTM network to extract out-word features in the sentence. Then, the decoder adopts a multi-head double attention layer to fuse the inner-word features and out-word features and produces the segmentation result. The evaluation experiment compares the proposed network with the baselines and explores the effectiveness of the sub-modules.

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Active Learning Approaches to Enhancing Neural Machine Translation
Yuekai Zhao | Haoran Zhang | Shuchang Zhou | Zhihua Zhang
Findings of the Association for Computational Linguistics: EMNLP 2020

Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer learning and iterative back-translation (IBT) under a limited human translation budget. We design a word frequency based acquisition function and combine it with a strong uncertainty based method. The combined method steadily outperforms all other acquisition functions in various scenarios. As far as we know, we are the first to do a large-scale study on actively training Transformer for NMT. Specifically, with a human translation budget of only 20% of the original parallel corpus, we manage to surpass Transformer trained on the entire parallel corpus in three language pairs.


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Co-Attention Based Neural Network for Source-Dependent Essay Scoring
Haoran Zhang | Diane Litman
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.


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Word Embedding for Response-To-Text Assessment of Evidence
Haoran Zhang | Diane Litman
Proceedings of ACL 2017, Student Research Workshop