Jianzhong Qi


2021

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Findings on Conversation Disentanglement
Rongxin Zhu | Jey Han Lau | Jianzhong Qi
Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association

Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and con-versation summarization. Framing it as a utterance-to-utterance classification problem — i.e. given an utterance of interest (UOI), find which past utterance it replies to — we explore a number of transformer-based models and found that BERT in combination with handcrafted features remains a strong baseline. We then build a multi-task learning model that jointly learns utterance-to-utterance and utterance-to-thread classification. Observing that the ground truth label (past utterance) is in the top candidates when our model makes an error, we experiment with using bipartite graphs as a post-processing step to learn how to best match a set of UOIs to past utterances. Experiments on the Ubuntu IRC dataset show that this approach has the potential to out-perform the conventional greedy approach of simply selecting the highest probability candidate for each UOI independently, indicating a promising future research direction.

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Evaluating Document Coherence Modeling
Aili Shen | Meladel Mistica | Bahar Salehi | Hang Li | Timothy Baldwin | Jianzhong Qi
Transactions of the Association for Computational Linguistics, Volume 9

Abstract While pretrained language models (LMs) have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clear. As a step towards a better understanding of their discourse modeling capabilities, we propose a sentence intrusion detection task. We examine the performance of a broad range of pretrained LMs on this detection task for English. Lacking a dataset for the task, we introduce INSteD, a novel intruder sentence detection dataset, containing 170,000+ documents constructed from English Wikipedia and CNN news articles. Our experiments show that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity. Further results over a novel linguistic probe dataset show that there is substantial room for improvement, especially in the cross- domain setting.

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Latent Reasoning for Low-Resource Question Generation
Xinting Huang | Jianzhong Qi | Yu Sun | Rui Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Grouped-Attention for Content-Selection and Content-Plan Generation
Bayu Distiawan Trisedya | Xiaojie Wang | Jianzhong Qi | Rui Zhang | Qingjun Cui
Findings of the Association for Computational Linguistics: EMNLP 2021

Content-planning is an essential part of data-to-text generation to determine the order of data mentioned in generated texts. Recent neural data-to-text generation models employ Pointer Networks to explicitly learn content-plan given a set of attributes as input. They use LSTM to encode the input, which assumes a sequential relationship in the input. This may be sub-optimal to encode a set of attributes, where the attributes have a composite structure: the attributes are disordered while each attribute value is an ordered list of tokens. We handle this problem by proposing a neural content-planner that can capture both local and global contexts of such a structure. Specifically, we propose a novel attention mechanism called GSC-attention. A key component of the GSC-attention is grouped-attention, which is token-level attention constrained within each input attribute that enables our proposed model captures both local and global context. Moreover, our content-planner explicitly learns content-selection, which is integrated into the content-planner to select the most important data to be included in the generated text via an attention masking procedure. Experimental results show that our model outperforms the competitors by 4.92%, 4.70%, and 16.56% in terms of Damerau-Levenshtein Distance scores on three real-world datasets.

2020

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Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation
Xinting Huang | Jianzhong Qi | Yu Sun | Rui Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Dialogue policy optimization often obtains feedback until task completion in task-oriented dialogue systems. This is insufficient for training intermediate dialogue turns since supervision signals (or rewards) are only provided at the end of dialogues. To address this issue, reward learning has been introduced to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards. This approach requires complete state-action annotations of human-to-human dialogues (i.e., expert demonstrations), which is labor intensive. To overcome this limitation, we propose a novel reward learning approach for semi-supervised policy learning. The proposed approach learns a dynamics model as the reward function which models dialogue progress (i.e., state-action sequences) based on expert demonstrations, either with or without annotations. The dynamics model computes rewards by predicting whether the dialogue progress is consistent with expert demonstrations. We further propose to learn action embeddings for a better generalization of the reward function. The proposed approach outperforms competitive policy learning baselines on MultiWOZ, a benchmark multi-domain dataset.

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Generalizable and Explainable Dialogue Generation via Explicit Action Learning
Xinting Huang | Jianzhong Qi | Yu Sun | Rui Zhang
Findings of the Association for Computational Linguistics: EMNLP 2020

Response generation for task-oriented dialogues implicitly optimizes two objectives at the same time: task completion and language quality. Conditioned response generation serves as an effective approach to separately and better optimize these two objectives. Such an approach relies on system action annotations which are expensive to obtain. To alleviate the need of action annotations, latent action learning is introduced to map each utterance to a latent representation. However, this approach is prone to over-dependence on the training data, and the generalization capability is thus restricted. To address this issue, we propose to learn natural language actions that represent utterances as a span of words. This explicit action representation promotes generalization via the compositional structure of language. It also enables an explainable generation process. Our proposed unsupervised approach learns a memory component to summarize system utterances into a short span of words. To further promote a compact action representation, we propose an auxiliary task that restores state annotations as the summarized dialogue context using the memory component. Our proposed approach outperforms latent action baselines on MultiWOZ, a benchmark multi-domain dataset.

2019

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Feature-guided Neural Model Training for Supervised Document Representation Learning
Aili Shen | Bahar Salehi | Jianzhong Qi | Timothy Baldwin
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association

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Modelling Uncertainty in Collaborative Document Quality Assessment
Aili Shen | Daniel Beck | Bahar Salehi | Jianzhong Qi | Timothy Baldwin
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

In the context of document quality assessment, previous work has mainly focused on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. To imitate people’s disagreement over inherently subjective tasks such as rating the quality of a Wikipedia article, a document quality assessment system should provide not only a prediction of the article quality but also the uncertainty over its predictions. This motivates us to measure the uncertainty in document quality predictions, in addition to making the label prediction. Experimental results show that both Gaussian processes (GPs) and random forests (RFs) can yield competitive results in predicting the quality of Wikipedia articles, while providing an estimate of uncertainty when there is inconsistency in the quality labels from the Wikipedia contributors. We additionally evaluate our methods in the context of a semi-automated document quality class assignment decision-making process, where there is asymmetric risk associated with overestimates and underestimates of document quality. Our experiments suggest that GPs provide more reliable estimates in this context.

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Neural Relation Extraction for Knowledge Base Enrichment
Bayu Distiawan Trisedya | Gerhard Weikum | Jianzhong Qi | Rui Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall.To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the-art baselines by 15.51% and 8.38% in terms of F1 score on two real-world datasets.

2018

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GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
Bayu Distiawan Trisedya | Jianzhong Qi | Rui Zhang | Wei Wang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6%, 6.0%, and 16.4% in three common metrics BLEU, METEOR, and TER, respectively.

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PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages
Yiqing Zhang | Jianzhong Qi | Rui Zhang | Chuandong Yin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Publication information in a researcher’s academic homepage provides insights about the researcher’s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8% in F1-score.

2017

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A Hybrid Model for Quality Assessment of Wikipedia Articles
Aili Shen | Jianzhong Qi | Timothy Baldwin
Proceedings of the Australasian Language Technology Association Workshop 2017

2016

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N-ary Biographical Relation Extraction using Shortest Path Dependencies
Gitansh Khirbat | Jianzhong Qi | Rui Zhang
Proceedings of the Australasian Language Technology Association Workshop 2016

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Disambiguating Entities Referred by Web Endpoints using Tree Ensembles
Gitansh Khirbat | Jianzhong Qi | Rui Zhang
Proceedings of the Australasian Language Technology Association Workshop 2016