Papers by Tyler Loakman

Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)

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Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

Challenge:	Existing graph neural networks (GNNs) teach message passing on a graph from text, resulting in a semantic gap between graph knowledge and text.
Approach:	They propose a framework to integrate external graph knowledge into chatbots by coagulating representations of both text and graph knowledge.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) baselines on dialogue generation.

With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models (2024.emnlp-main)

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Tyler Loakman, Yucheng Li, Chenghua Lin

Multimodality

Challenge:	Large Language Models and Vision Language Model (VLMs) have demonstrated aptitude as potential substitutes for human participants in psycholinguistic experiments.
Approach:	They examine whether large language models and vision language models implicitly understand sound-based phenomena via orthography and imagery alone.
Outcome:	The proposed models demonstrate sound symbolism and ability to "hear" using language and vision modules.

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth (2025.emnlp-main)

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Yang Wang, Chenghao Xiao, Chia-Yi Hsiao, Zi Yan Chang, Chi-Li Chen, Tyler Loakman, Chenghua Lin

Responsible & Trustworthy NLP

Challenge:	Despite excelling at many natural language processing tasks, large language models fail to grasp the layered semantics of Drivelological text.
Approach:	They construct a benchmark dataset of over 1,200+ carefully curated and diverse examples across English, Mandarin, Spanish, French, Japanese, and Korean to examine their Drivelological characteristics.
Outcome:	The proposed models lack conceptual understanding and lack conceptual and semantic accuracy.

Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)

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Henglin Huang, Chen Tang, Tyler Loakman, Frank Guerin, Chenghua Lin

Representation Learning

Challenge:	Current neural models for Chinese story generation struggle to generate high-quality long text narratives due to ambiguity in syntactically parsing the Chinese language.
Approach:	They propose a framework that enhances the feature capturing mechanism by informing the generation model of dependencies between words and additionally augmenting the semantic representation learning through synonym denoising training.
Outcome:	The proposed framework outperforms the state-of-the-art Chinese generation models on all evaluation metrics, showing that it enhances dependency and semantic representation learning.

MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language (2024.emnlp-main)

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Shun Wang, Ge Zhang, Han Wu, Tyler Loakman, Wenhao Huang, Chenghua Lin

Multilinguality

Challenge:	Existing evaluation methods focus on fluency and factual reliability, while neglecting figurative quality.
Approach:	They propose a set of human evaluation metrics focused on the translation of figurative language and a parallel metaphor corpus generated by post-editing.
Outcome:	The proposed evaluation protocol estimates four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality.

The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation (2023.findings-emnlp)

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Tyler Loakman, Aaron Maladry, Chenghua Lin

Sentiment Analysis

Challenge:	Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system, but its quality is often brought into question.
Approach:	They argue that the generation of more esoteric forms of language constitutes a subdomain where the characteristics of selected evaluator panels are of utmost importance.
Outcome:	The proposed system generates coherent and well-formed text of a particular type, usually given an input such as a prompt, outline, topic, or data.

TwistList: Resources and Baselines for Tongue Twister Generation (2023.acl-short)

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Tyler Loakman, Chen Tang, Chenghua Lin

Syntactic Text Processing

Challenge:	Previous work in phonetically-grounded language generation has focused on domains such as lyrics and poetry.
Approach:	They propose to use TwistList to generate phonetically constrained tongue twisters, a large annotated dataset consisting of 2.1K+ human-authored examples.
Outcome:	The proposed models perform better than pre-trained models with limited training and data and no explicit phonetic knowledge.

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

Language Models

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes (2025.findings-emnlp)

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Tyler Loakman, William Thorne, Chenghua Lin

Explainability & Interpretability in NLP

Challenge:	Existing work on humour explanation has focused on short pun-based jokes, but Large Language Models (LLMs) are not capable of generating adequate explanations of all joke types.
Approach:	They compare the ability of Large Language Models (LLMs) to explain humour from simple puns to complex topical humor that requires esoteric knowledge of real-world entities and events.
Outcome:	The proposed models are incapable of generating adequate explanations of all joke types, highlighting the narrow focus of most existing work on overly simple joke forms.

NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)

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Chen Tang, Zhihao Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

Multimodality

Challenge:	Current approaches to story generation are based on end-to-end neural generation models, such as BART, to generate event sequences.
Approach:	They propose a novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor.
Outcome:	The proposed framework outperforms state-of-the-art (SOTA) event planning approaches on multiple criteria and compares with existing models on the downstream task of story generation.