Haozhe Ji


2021

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JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Pei Ke | Haozhe Ji | Yu Ran | Xin Cui | Liwei Wang | Linfeng Song | Xiaoyan Zhu | Minlie Huang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer
Haozhe Ji | Minlie Huang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Despite the recent advances in applying pre-trained language models to generate high-quality texts, generating long passages that maintain long-range coherence is yet challenging for these models. In this paper, we propose DiscoDVT, a discourse-aware discrete variational Transformer to tackle the incoherence issue. DiscoDVT learns a discrete variable sequence that summarizes the global structure of the text and then applies it to guide the generation process at each decoding step. To further embed discourse-aware information into the discrete latent representations, we introduce an auxiliary objective to model the discourse relations within the text. We conduct extensive experiments on two open story generation datasets and demonstrate that the latent codes learn meaningful correspondence to the discourse structures that guide the model to generate long texts with better long-range coherence.

2020

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Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph
Haozhe Ji | Pei Ke | Shaohan Huang | Furu Wei | Xiaoyan Zhu | Minlie Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.

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SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge
Pei Ke | Haozhe Ji | Siyang Liu | Xiaoyan Zhu | Minlie Huang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pre-trained models. We first propose a context-aware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWordNet. Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation. Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks.

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Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths
Haozhe Ji | Pei Ke | Shaohan Huang | Furu Wei | Minlie Huang
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Commonsense explanation generation aims to empower the machine’s sense-making capability by generating plausible explanations to statements against commonsense. While this task is easy to human, the machine still struggles to generate reasonable and informative explanations. In this work, we propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation. To facilitate the reasoning process, we utilize external commonsense knowledge to build the connection between a statement and the bridge concepts by extracting and pruning multi-hop paths to build a subgraph. We design a bridge concept extraction model that first scores the triples, routes the paths in the subgraph, and further selects bridge concepts with weak supervision at both the triple level and the concept level. We conduct experiments on the commonsense explanation generation task and our model outperforms the state-of-the-art baselines in both automatic and human evaluation.

2018

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Denoising Distantly Supervised Open-Domain Question Answering
Yankai Lin | Haozhe Ji | Zhiyuan Liu | Maosong Sun
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from the most relevant paragraph. They ignore the rich information contained in other paragraphs. Moreover, distant supervision data inevitably accompanies with the wrong labeling problem, and these noisy data will substantially degrade the performance of DS-QA. To address these issues, we propose a novel DS-QA model which employs a paragraph selector to filter out those noisy paragraphs and a paragraph reader to extract the correct answer from those denoised paragraphs. Experimental results on real-world datasets show that our model can capture useful information from noisy data and achieve significant improvements on DS-QA as compared to all baselines.