Jiamou Liu


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From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory
Jaron Mar | Jiamou Liu
Findings of the Association for Computational Linguistics: NAACL 2022

Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality. Fuzzy trace theory (FTT) is a powerful paradigm that explains human decision-making by incorporating gists, i.e., fuzzy representations of information which capture only its quintessential meaning. Inspired by Broniatowski and Reyna’s FTT cognitive model, we propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. In particular, we introduce Category-2-Vector to learn categorical gists and categorical sentiments, and demonstrate how our computational model can be optimised to predict risky decision-making in groups and individuals.

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TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs
Qianqian Qi | Zhenyun Deng | Yonghua Zhu | Lia Jisoo Lee | Michael Witbrock | Jiamou Liu
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

We introduce TaKG, a new table-to-text generation dataset with the following highlights: (1) TaKG defines a long-text (paragraph-level) generation task as opposed to well-established short-text (sentence-level) generation datasets. (2) TaKG is the first large-scale dataset for this task, containing three application domains and ~750,000 samples. (3) To address the divergence phenomenon, TaKG enhances table input using external knowledge graphs, extracted by a new Wikidata-based method. We then propose a new Transformer-based multimodal sequence-to-sequence architecture for TaKG that integrates two pretrained language models RoBERTa and GPT-2. Our model shows reliable performance on long-text generation across a variety of metrics, and outperforms existing models for short-text generation tasks.


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Generating Relevant and Coherent Dialogue Responses using Self-Separated Conditional Variational AutoEncoders
Bin Sun | Shaoxiong Feng | Yiwei Li | Jiamou Liu | Kan Li
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables. However, due to the inherent one-to-many and many-to-one phenomena in human dialogues, the sampled latent variables may not correctly reflect the contexts’ semantics, leading to irrelevant and incoherent generated responses. To resolve this problem, we propose Self-separated Conditional Variational AutoEncoder (abbreviated as SepaCVAE) that introduces group information to regularize the latent variables, which enhances CVAE by improving the responses’ relevance and coherence while maintaining their diversity and informativeness. SepaCVAE actively divides the input data into groups, and then widens the absolute difference between data pairs from distinct groups, while narrowing the relative distance between data pairs in the same group. Empirical results from automatic evaluation and detailed analysis demonstrate that SepaCVAE can significantly boost responses in well-established open-domain dialogue datasets.