Papers by Frank Guerin
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation (2023.acl-long)
Copied to clipboard
| 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. |
Compressing Context to Enhance Inference Efficiency of Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable power and impressive generalisation abilities across various tasks. |
| Approach: | They propose a method that prunes redundancies in the input context to make the input more compact. |
| Outcome: | The proposed method reduces memory and inference time while maintaining comparable performance compared to full context. |
Word Embedding and WordNet Based Metaphor Identification and Interpretation (P18-1)
Copied to clipboard
| Challenge: | Existing models cannot identify exact metaphorical words within a sentence . current models do not rely on hand-crafted knowledge for training . |
| Approach: | They propose an unsupervised learning method that identifies and interprets metaphors at word-level without preprocessing. |
| Outcome: | The proposed method outperforms baseline models in two translation systems for English to Chinese showing that it paraphrases metaphors into their literal counterparts. |
Metaphor Detection via Explicit Basic Meanings Modelling (2023.acl-short)
Copied to clipboard
| Challenge: | Existing methods for metaphor detection use the aggregated meaning of a word to approximate its basic meaning. |
| Approach: | They propose a method which models the basic meaning of a word based on literal annotations and compares this with the contextual meaning in a target sentence to identify metaphors. |
| Outcome: | The proposed method outperforms the state-of-the-art method significantly in the F1 score and even reaches the theoretical upper bound on the VUA18 benchmark. |
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)
Copied to clipboard
| 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. |
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for story generation still suffer from problems of relevance and coherence. |
| Approach: | They propose a novel neural generation model which maps contextual and event features to event sequences with a cross-attention mechanism and exploits logical relatedness between events. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic and human evaluations and shows that it can leverage contextual and event features. |
NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)
Copied to clipboard
| 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. |
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories (P19-1)
Copied to clipboard
| Challenge: | Existing sequence tagging models do not explicitly exploit linguistic theories of metaphor identification. |
| Approach: | They propose to exploit linguistic theories of metaphor identification in deep neural networks to improve model performance. |
| Outcome: | The proposed models achieve state-of-the-art in end-to-end metaphor identification on three datasets. |
GPTEval: A Survey on Assessments of ChatGPT and GPT-4 (2024.lrec-main)
Copied to clipboard
| Challenge: | emergence of ChatGPT has generated speculation about its potential to disrupt social and economic systems. |
| Approach: | They analyze prior assessments of ChatGPT and GPT-4 to analyze their language and reasoning abilities, scientific knowledge, ethical considerations and existing evaluation methods. |
| Outcome: | The proposed model performs satisfactorily in science knowledge and can answer open questions. |
CM-Gen: A Neural Framework for Chinese Metaphor Generation with Explicit Context Modelling (2022.coling-1)
Copied to clipboard
| Challenge: | Nominal metaphors are commonly used in human language and have been shown to be effective in persuading, expressing emotion, and stimulating interest. |
| Approach: | They propose a multitask framework which optimizes three tasks: NM identification, NM component identification, and NM generation. |
| Outcome: | The proposed framework outperforms baselines on consistency and creativity on the NM generation task in Chinese. |
Metaphor Detection with Effective Context Denoising (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing models focus on semantically relevant information and provide a target-oriented parse tree structure for metaphor detection. |
| Approach: | They propose a new model which introduces a target-oriented parse tree structure for metaphor detection. |
| Outcome: | The proposed model achieves state-of-the-art on several main metaphor datasets and compares with other methods. |
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing models for concept-level metaphor detection lack explicit knowledge of FrameNet . Metaphor detection is a pervasive linguistic device that is used in cognitive and communicative functions of language. |
| Approach: | They propose a BERT-based model that explicitly learns FrameNet Embeddings for metaphor detection. |
| Outcome: | The proposed model is more explainable and interpretable than existing models. |
An Open-Source Data Contamination Report for Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing contamination analysis is conducted internally by large language model developers and lacks transparency and completeness. |
| Approach: | They present a data contamination report for 15 popular large language models . they propose an open-source pipeline to perform contamination analysis on customised data . |
| Outcome: | The proposed pipeline enables the community to perform contamination analysis on customised data and models. |