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Feiteng Mu

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3 papers

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IJCAI Conference 2022 Conference Paper

Enhancing Text Generation via Multi-Level Knowledge Aware Reasoning

Feiteng Mu
Wenjie Li

How to generate high-quality textual content is a non-trivial task. Existing methods generally generate text by grounding on word-level knowledge. However, word-level knowledge cannot express multi-word text units, hence existing methods may generate low-quality and unreasonable text. In this paper, we leverage event-level knowledge to enhance text generation. However, event knowledge is very sparse. To solve this problem, we split a coarse-grained event into fine-grained word components to obtain the word-level knowledge among event components. The word-level knowledge models the interaction among event components, which makes it possible to reduce the sparsity of events. Based on the event-level and the word-level knowledge, we devise a multi-level knowledge aware reasoning framework. Specifically, we first utilize event knowledge to make event-based content planning, i. e. , select reasonable event sketches conditioned by the input text. Then, we combine the selected event sketches with the word-level knowledge for text generation. We validate our method on two widely used datasets, experimental results demonstrate the effectiveness of our framework to text generation.

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IJCAI Conference 2019 Conference Paper

Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns

Zhipeng Xie
Feiteng Mu

Existing approaches to causal embeddings rely heavily on hand-crafted high-precision causal patterns, leading to limited coverage. To solve this problem, this paper proposes a method to boost causal embeddings by exploring potential verb-mediated causal patterns. It first constructs a seed set of causal word pairs, then uses them as supervision to characterize the causal strengths of extracted verb-mediated patterns, and finally exploits the weighted extractions by those verb-mediated patterns in the construction of boosted causal embeddings. Experimental results have shown that the boosted causal embeddings outperform several state-of-the-arts significantly on both English and Chinese. As by-products, the top-ranked patterns coincide with human intuition about causality.

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AAAI Conference 2019 Conference Paper

Distributed Representation of Words in Cause and Effect Spaces

Zhipeng Xie
Feiteng Mu

This paper focuses on building up distributed representation of words in cause and effect spaces, a task-specific word embedding technique for causality. The causal embedding model is trained on a large set of cause-effect phrase pairs extracted from raw text corpus via a set of high-precision causal patterns. Three strategies are proposed to transfer the positive or negative labels from the level of phrase pairs to the level of word pairs, leading to three causal embedding models (Pairwise-Matching, Max-Matching, and Attentive- Matching) correspondingly. Experimental results have shown that Max-Matching and Attentive-Matching models significantly outperform several state-of-the-art competitors by a large margin on both English and Chinese corpora.

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