Papers by Julian Hough
Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues (2024.lrec-main)
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| Challenge: | Existing large language models can be fine-tuned offline but are large and resource-intensive. |
| Approach: | They propose to use a simple reference resolver to simulate a conceptual pact process over time with different conversation pairs. |
| Outcome: | The proposed model performs better than a pre-trained model with exhaustive retraining after each prediction, while being more transparent, faster and less resource-intensive. |
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning (2020.coling-main)
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| Challenge: | Using a multi-task learning framework, we train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Approach: | They propose a multi-task learning framework to train a universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. |
| Outcome: | The proposed model outperforms individual tasks and delivers competitive performance. |
Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental (2021.acl-long)
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| Challenge: | Currently, Transformer-based text classifiers are not suitable for live incremental processing, operating only on the level of complete sentence inputs. |
| Approach: | They propose to introduce a method for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. |
| Outcome: | The proposed method maintains high non-incremental performance while operating strictly incrementally. |