Qinglin Zhang


2024

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Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control
Yunzhe Li | Qian Chen | Weixiang Yan | Wen Wang | Qinglin Zhang | Hari Sundaram
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Existing works on outline-conditioned text generation typically aim to generate text using provided outlines as rough sketches, such as keywords and phrases. However, these approaches make it challenging to control the quality of text generation and assess consistency between outlines and generated texts due to lack of clarity and rationality of the rough outlines. In this paper, we introduce a novel text generation task called Precise Outline-conditioned Generation, which requires generating stories based on specific, sentence-level outlines. To facilitate research on this task, we construct two new datasets, WPOG and CDM. We provide strong baselines based on fine-tuning models such as BART and GPT-2, and evaluating zero-shot performance of models such as ChatGPT and Vicuna. Furthermore, we identify an issue of imbalanced utilization of the outline information in the precise outline-conditioned generation, which is ubiquitously observed across fine-tuned models and zero-shot inference models. To address this issue, we propose an explicit outline utilization control approach and a novel framework that leverages the task duality between summarization and generation. Experimental results show that the proposed approaches effectively alleviate the issue of imbalanced outline utilization and enhance the quality of precise outline-conditioned text generation for both fine-tuning and zero-shot settings.

2023

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Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling
Hai Yu | Chong Deng | Qinglin Zhang | Jiaqing Liu | Qian Chen | Wen Wang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic segmentation is critical for obtaining structured documents and improving down- stream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural models have greatly promoted the development of long document topic segmentation, but leaving the deeper relationship between coherence and topic segmentation underexplored. Therefore, this paper enhances the ability of supervised models to capture coherence from both logical structure and semantic similarity perspectives to further improve the topic segmentation performance, proposing Topic-aware Sentence Structure Prediction (TSSP) and Contrastive Semantic Similarity Learning (CSSL). Specifically, the TSSP task is proposed to force the model to comprehend structural information by learning the original relations between adjacent sentences in a disarrayed document, which is constructed by jointly disrupting the original document at topic and sentence levels. Moreover, we utilize inter- and intra-topic information to construct contrastive samples and design the CSSL objective to ensure that the sentences representations in the same topic have higher similarity, while those in different topics are less similar. Extensive experiments show that the Longformer with our approach significantly outperforms old state-of-the-art (SOTA) methods. Our approach improve F1 of old SOTA by 3.42 (73.74 77.16) and reduces Pk by 1.11 points (15.0 13.89) on WIKI-727K and achieves an average relative reduction of 4.3% on Pk on WikiSection. The average relative Pk drop of 8.38% on two out-of-domain datasets also demonstrates the robustness of our approach.

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Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Qian Chen | Wen Wang | Qinglin Zhang | Siqi Zheng | Chong Deng | Hai Yu | Jiaqing Liu | Yukun Ma | Chong Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis reveals that the sentence embeddings from BERT suffer from a bias towards uninformative words, limiting the performance in semantic textual similarity (STS) tasks. To address this bias, we propose a simple and efficient unsupervised approach, Diagonal Attention Pooling (Ditto), which weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. Ditto can be easily applied to any pre-trained language model as a postprocessing operation. Compared to prior sentence embedding approaches, Ditto does not add parameters nor requires any learning. Empirical evaluations demonstrate that our proposed Ditto can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks.