Guanyi Chen


2020

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Listener’s Social Identity Matters in Personalised Response Generation
Guanyi Chen | Yinhe Zheng | Yupei Du
Proceedings of the 13th International Conference on Natural Language Generation

Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human beings adjust the way of speaking based on not only who they are but also whom they are talking to. In other words, when modelling personalised dialogues, it might be favourable if we also take the listener’s social identity into consideration. To validate this idea, we use gender as a typical example of a social variable to investigate how the listener’s identity influences the language used in Chinese dialogues on social media. Also, we build personalised generators. The experiment results demonstrate that the listener’s identity indeed matters in the language use of responses and that the response generator can capture such differences in language use. More interestingly, by additionally modelling the listener’s identity, the personalised response generator performs better in its own identity.

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Lessons from Computational Modelling of Reference Production in Mandarin and English
Guanyi Chen | Kees van Deemter
Proceedings of the 13th International Conference on Natural Language Generation

Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in Mandarin was introduced. In the present paper, we annotate this corpus, evaluate classic REG algorithms on it, and compare the results with earlier results on the evaluation of REG for English referring expressions. Next, we offer an in-depth analysis of the corpus, focusing on issues that arise from the grammar of Mandarin. We discuss shortcomings of previous REG evaluations that came to light during our investigation and we highlight some surprising results. Perhaps most strikingly, we found a much higher proportion of under-specified expressions than previous studies had suggested, not just in Mandarin but in English as well.

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Gradations of Error Severity in Automatic Image Descriptions
Emiel van Miltenburg | Wei-Ting Lu | Emiel Krahmer | Albert Gatt | Guanyi Chen | Lin Li | Kees van Deemter
Proceedings of the 13th International Conference on Natural Language Generation

Earlier research has shown that evaluation metrics based on textual similarity (e.g., BLEU, CIDEr, Meteor) do not correlate well with human evaluation scores for automatically generated text. We carried out an experiment with Chinese speakers, where we systematically manipulated image descriptions to contain different kinds of errors. Because our manipulated descriptions form minimal pairs with the reference descriptions, we are able to assess the impact of different kinds of errors on the perceived quality of the descriptions. Our results show that different kinds of errors elicit significantly different evaluation scores, even though all erroneous descriptions differ in only one character from the reference descriptions. Evaluation metrics based solely on textual similarity are unable to capture these differences, which (at least partially) explains their poor correlation with human judgments. Our work provides the foundations for future work, where we aim to understand why different errors are seen as more or less severe.

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Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
Ruizhe Li | Xiao Li | Guanyi Chen | Chenghua Lin
Proceedings of the 28th International Conference on Computational Linguistics

The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse (or KL loss vanishing) happens when the VAE is used in text modelling, where the approximate posterior collapses to the prior, and the model will totally ignore the latent variables and be degraded to a plain language model during text generation. Such an issue is particularly prevalent when RNN-based VAE models are employed for text modelling. In this paper, we propose a simple, generic architecture called Timestep-Wise Regularisation VAE (TWR-VAE), which can effectively avoid posterior collapse and can be applied to any RNN-based VAE models. The effectiveness and versatility of our model are demonstrated in different tasks, including language modelling and dialogue response generation.

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DGST: a Dual-Generator Network for Text Style Transfer
Xiao Li | Guanyi Chen | Chenghua Lin | Ruizhe Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several strong baselines with more complicated architecture designs.

2019

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A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Ruizhe Li | Chenghua Lin | Matthew Collinson | Xiao Li | Guanyi Chen
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition. In this paper, we propose a dual-attention hierarchical recurrent neural network for DA classification. Our model is partially inspired by the observation that conversational utterances are normally associated with both a DA and a topic, where the former captures the social act and the latter describes the subject matter. However, such a dependency between DAs and topics has not been utilised by most existing systems for DA classification. With a novel dual task-specific attention mechanism, our model is able, for utterances, to capture information about both DAs and topics, as well as information about the interactions between them. Experimental results show that by modelling topic as an auxiliary task, our model can significantly improve DA classification, yielding better or comparable performance to the state-of-the-art method on three public datasets.

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QTUNA: A Corpus for Understanding How Speakers Use Quantification
Guanyi Chen | Kees van Deemter | Silvia Pagliaro | Louk Smalbil | Chenghua Lin
Proceedings of the 12th International Conference on Natural Language Generation

A prominent strand of work in formal semantics investigates the ways in which human languages quantify over the elements of a set, as when we say “All A are B ”, “All except two A are B ”, “Only a few of the A are B ” and so on. Our aim is to build Natural Language Generation algorithms that mimic humans’ use of quantified expressions. To inform these algorithms, we conducted on a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions. We discuss how these experiments were conducted and what corpora they gave rise to. We conduct an informal analysis of the corpora, and offer an initial assessment of the challenges that these corpora pose for Natural Language Generation. The dataset is available at: https://github.com/a-quei/qtuna.

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A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG
Guanyi Chen | Jin-Ge Yao
Proceedings of the 12th International Conference on Natural Language Generation

Automatic natural language generation systems need to use the contextually-appropriate verbs when describing different kinds of facts or events, which has triggered research interest on verb selection for data-to-text generation. In this paper, we discuss a few limitations of the current task settings and the evaluation metrics. We also provide two simple, efficient, interpretable baseline approaches for statistical selection of trend verbs, which give a strong performance on both previously used evaluation metrics and our new evaluation.

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Generating Quantified Descriptions of Abstract Visual Scenes
Guanyi Chen | Kees van Deemter | Chenghua Lin
Proceedings of the 12th International Conference on Natural Language Generation

Quantified expressions have always taken up a central position in formal theories of meaning and language use. Yet quantified expressions have so far attracted far less attention from the Natural Language Generation community than, for example, referring expressions. In an attempt to start redressing the balance, we investigate a recently developed corpus in which quantified expressions play a crucial role; the corpus is the result of a carefully controlled elicitation experiment, in which human participants were asked to describe visually presented scenes. Informed by an analysis of this corpus, we propose algorithms that produce computer-generated descriptions of a wider class of visual scenes, and we evaluate the descriptions generated by these algorithms in terms of their correctness, completeness, and human-likeness. We discuss what this exercise can teach us about the nature of quantification and about the challenges posed by the generation of quantified expressions.

2018

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ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
Rui Mao | Guanyi Chen | Ruizhe Li | Chenghua Lin
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set.

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SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin
Guanyi Chen | Kees van Deemter | Chenghua Lin
Proceedings of the 11th International Conference on Natural Language Generation

We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009). We explain the core grammar (morphology and syntax) and the lexicon of SimpleNLG-ZH, which is very different from English and other languages for which SimpleNLG engines have been built. The system was evaluated by regenerating expressions from a body of test sentences and a corpus of human-authored expressions. Human evaluation was conducted to estimate the quality of regenerated sentences.

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Modelling Pro-drop with the Rational Speech Acts Model
Guanyi Chen | Kees van Deemter | Chenghua Lin
Proceedings of the 11th International Conference on Natural Language Generation

We extend the classic Referring Expressions Generation task by considering zero pronouns in “pro-drop” languages such as Chinese, modelling their use by means of the Bayesian Rational Speech Acts model (Frank and Goodman, 2012). By assuming that highly salient referents are most likely to be referred to by zero pronouns (i.e., pro-drop is more likely for salient referents than the less salient ones), the model offers an attractive explanation of a phenomenon not previously addressed probabilistically.