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Found 47 papers, 6 papers with code
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.
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings
Word embeddings have advanced the state of the art in NLP across numerous tasks.
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.
Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse
First, we discuss the most common linguistic perspectives on the concept of recency and propose a taxonomy of recency metrics employed in Machine Learning studies for choosing the form of referring expressions in discourse context.
Towards Generating Effective Explanations of Logical Formulas: Challenges and Strategies
While the problem of natural language generation from logical formulas has a long tradition, thus far little attention has been paid to ensuring that the generated explanations are optimally effective for the user.
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches
This paper presents our work in long and short form choice, a significant question of lexical choice, which plays an important role in many Natural Language Understanding tasks.
Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases
In this paper, we focus on the omission of the plurality and definiteness markers in Chinese noun phrases (NPs) to investigate the predictability of their intended meaning given the contexts.
Intrinsic Task-based Evaluation for Referring Expression Generation
Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on \textsc{webnlg}, Referring Expressions (REs) generated by the state-of-the-art neural models were not only indistinguishable from the REs in \textsc{webnlg} but also from the REs generated by a simple rule-based system.
Textual Summarisation of Large Sets: Towards a General Approach
We are developing techniques to generate summary descriptions of sets of objects.
The Pitfalls of Defining Hallucination
To substantiate this claim, I examine current classifications of hallucination and omission in Data-text NLG, and I propose a logic-based synthesis of these classfications.
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.
Does ChatGPT have Theory of Mind?
Theory of Mind (ToM) is the ability to understand human thinking and decision-making, an ability that plays a crucial role in social interaction between people, including linguistic communication.
Missing Information, Unresponsive Authors, Experimental Flaws: The Impossibility of Assessing the Reproducibility of Previous Human Evaluations in NLP
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible.
Interpreting Vision and Language Generative Models with Semantic Visual Priors
When applied to Image-to-text models, interpretability methods often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence.
HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales
We present the High-Level Dataset a dataset extending 14997 images from the COCO dataset, aligned with a new set of 134, 973 human-annotated (high-level) captions collected along three axes: scenes, actions, and rationales.
Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions
Image captioning models tend to describe images in an object-centric way, emphasising visible objects.
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.
The Role of Explanatory Value in Natural Language Processing
A key aim of science is explanation, yet the idea of explaining language phenomena has taken a backseat in mainstream Natural Language Processing (NLP) and many other areas of Artificial Intelligence.
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings
Word embeddings have advanced the state of the art in NLP across numerous tasks.
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.
What Vision-Language Models `See' when they See Scenes
Images can be described in terms of the objects they contain, or in terms of the types of scene or place that they instantiate.
What can Neural Referential Form Selectors Learn?
Despite achieving encouraging results, neural Referring Expression Generation models are often thought to lack transparency.
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context
This paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context.
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.
A Text Reassembling Approach to Natural Language Generation
Recent years have seen a number of proposals for performing Natural Language Generation (NLG) based in large part on statistical techniques.
Choosing between Long and Short Word Forms in Mandarin
Between 80{\%} and 90{\%} of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013).
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.
Generating Summaries of Sets of Consumer Products: Learning from Experiments
We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset.
Statistical NLG for Generating the Content and Form of Referring Expressions
This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully.
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).
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).
Meteorologists and Students: A resource for language grounding of geographical descriptors
We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation.
Investigating the content and form of referring expressions in Mandarin: introducing the Mtuna corpus
East Asian languages are thought to handle reference differently from languages such as English, particularly in terms of the marking of definiteness and number.
An Empirical Approach for Modeling Fuzzy Geographical Descriptors
We present a novel heuristic approach that defines fuzzy geographical descriptors using data gathered from a survey with human subjects.
