Papers by Xinnuo Xu
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
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| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
| Approach: | They propose to introduce a measure of coherence as the GloVe embedding similarity between dialogue context and generated response to improve output diversity. |
| Outcome: | The proposed model improves on the OpenSubtitles corpus in terms of BLEU score and diversity metrics. |
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation (2021.acl-long)
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| Challenge: | Large-scale conversational systems typically generate unnatural, robotic responses using template-based approaches. |
| Approach: | They propose a data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model to automatically create MR-to-Text data from open-domain texts. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. |
MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization (2021.findings-emnlp)
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| Challenge: | Current news summarization systems often contain 'extrinsic hallucinations', i.e. facts that are not present in the source document, which are often derived via world knowledge. |
| Approach: | They propose to use multiple supplementary resource documents to assist the task by pairing a single document with a human authored summary as the summary. |
| Outcome: | The proposed model reduces 55% of hallucinations when compared to single-document summarization models trained on the main article only. |
Compositional Generalization for Data-to-Text Generation (2023.findings-emnlp)
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| Challenge: | Data-to-text generation models can be used to generate textual descriptions from structured data . despite advances, systems struggle when confronted with unseen combinations of predicates . |
| Approach: | They propose a data-to-text generation model that addresses compositional generalization by clustering predicates into groups. |
| Outcome: | The proposed model outperforms T5-baselines in all evaluation metrics. |
Graph Guided Question Answer Generation for Procedural Question-Answering (2024.eacl-long)
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Hai Pham, Isma Hadji, Xinnuo Xu, Ziedune Degutyte, Jay Rainey, Evangelos Kazakos, Afsaneh Fazly, Georgios Tzimiropoulos, Brais Martinez
| Challenge: | a new method for question-answer generation from procedural text is sub-optimal for training QA models. |
| Approach: | They propose a method for generating exhaustive and high-quality training data from procedural text . they use procedural data to represent each step and the overall flow of the procedure as graphs . |
| Outcome: | The proposed method outperforms existing methods on task-specific question answering tasks. |
Fact-based Content Weighting for Evaluating Abstractive Summarisation (2020.acl-main)
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| Challenge: | Abstractive summarisation is notoriously hard to evaluate since word-overlap-based metrics are insufficient. |
| Approach: | They propose a new evaluation metric which is based on fact-level content weighting, relating the facts of the document to the facts in the summary. |
| Outcome: | The proposed evaluation metric is highly correlated to human perception and compares favourably to the recent manual highlight-based metric of Hardy et al. |
AggGen: Ordering and Aggregating while Generating (2021.acl-long)
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| Challenge: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
| Approach: | AggGen re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. |
| Outcome: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |