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Found 26 papers, 10 papers with code
Listener’s Social Identity Matters in Personalised Response Generation
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity.
Gradations of Error Severity in Automatic Image Descriptions
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.
Using BERT for choosing classifiers in Mandarin
Choosing the most suitable classifier in a linguistic context is a well-known problem in the production of Mandarin and many other languages.
GPTEval: A Survey on Assessments of ChatGPT and GPT-4
The emergence of ChatGPT has generated much speculation in the press about its potential to disrupt social and economic systems.
Models of reference production: How do they withstand the test of time?
We conclude that GREC can no longer be regarded as offering a reliable assessment of models' ability to mimic human reference production, because the results are highly impacted by the choice of corpus and evaluation metrics.
Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset
Previous work on Neural Referring Expression Generation (REG) all uses WebNLG, an English dataset that has been shown to reflect a very limited range of referring expression (RE) use.
Understanding the Use of Quantifiers in Mandarin
We introduce a corpus of short texts in Mandarin, in which quantified expressions figure prominently.
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)
We have noticed that Marek et al. (2021) try to re-implement our paper Zheng et al. (2020a) in their work "OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation".
Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems
In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG.
Affective Decoding for Empathetic Response Generation
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems.
MMChat: Multi-Modal Chat Dataset on Social Media
To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf.
What can Neural Referential Form Selectors Learn?
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency.
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications.
Lessons from Computational Modelling of Reference Production in Mandarin and English
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.
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation
The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences.
DGST: a Dual-Generator Network for Text Style Transfer
We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer.
Listener's Social Identity Matters in Personalised Response Generation
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity.
A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG
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.
QTUNA: A Corpus for Understanding How Speakers Use Quantification
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.
Generating Quantified Descriptions of Abstract Visual Scenes
Quantified expressions have always taken up a central position in formal theories of meaning and language use.
Out-of-domain Detection for Natural Language Understanding in Dialog Systems
Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.
Natural Language Understanding
Out of Distribution (OOD) Detection
+1
Personalized Dialogue Generation with Diversified Traits
In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues.
Modelling Pro-drop with the Rational Speech Acts Model
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).
SimpleNLG-ZH: a Linguistic Realisation Engine for Mandarin
We introduce SimpleNLG-ZH, a realisation engine for Mandarin that follows the software design paradigm of SimpleNLG (Gatt and Reiter, 2009).
A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
Recognising dialogue acts (DA) is important for many natural language processing tasks such as dialogue generation and intention recognition.
Ranked #4 on
Dialogue Act Classification
on Switchboard corpus
ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes.
