publications

2023

1. EMNLP

   Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification

   Cripwell, Liam, Legrand, Joël, and Gardent, Claire

   In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing 2023

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2. SIGDIAL/INLG

   The 2023 WebNLG Shared Task on Low Resource Languages. Overview and Evaluation Results (WebNLG 2023)

   Cripwell, Liam, Belz, Anya, Gardent, Claire, Gatt, Albert, Borg, Claudia, Borg, Marthese, Judge, John, Lorandi, Michela, Nikiforovskaya, Anna, and Soto Martinez, William

   In Proceedings of the Workshop on Multimodal, Multilingual Natural Language Generation and Multilingual WebNLG Challenge (MM-NLG 2023) 2023

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   The WebNLG task consists of mapping a knowledge graph to a text verbalising the con- tent of that graph. The 2017 WebNLG edi- tion required participating systems to gener- ate English text from a set of DBpedia triples, while the 2020 WebNLG+ challenge addition- ally included generation into Russian and se- mantic parsing of English and Russian texts. In contrast, WebNLG 2023 focuses on four under-resourced languages which are severely under-represented in research on text genera- tion, namely Breton, Irish, Maltese and Welsh. In addition, WebNLG 2023 once again includes Russian. In this paper, we present the organi- sation of the shared task (data, timeline, eval- uation), briefly describe the participating sys- tems and summarise results for participating systems.

3. ACL

   Context-Aware Document Simplification

   Cripwell, Liam, Legrand, Joël, and Gardent, Claire

   In Findings of the Association for Computational Linguistics: ACL 2023 2023

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   To date, most work on text simplification has focused on sentence-level inputs. Early attempts at document simplification merely applied these approaches iteratively over the sentences of a document. However, this fails to coherently preserve the discourse structure, leading to suboptimal output quality. Recently, strategies from controllable simplification have been leveraged to achieve state-of-the-art results on document simplification by first generating a document-level plan (a sequence of sentence-level simplification operations) and using this plan to guide sentence-level simplification downstream. However, this is still limited in that the simplification model has no direct access to the local inter-sentence document context, likely having a negative impact on surface realisation. We explore various systems that use document context within the simplification process itself, either by iterating over larger text units or by extending the system architecture to attend over a high-level representation of document context. In doing so, we achieve state-of-the-art performance on the document simplification task, even when not relying on plan-guidance. Further, we investigate the performance and efficiency tradeoffs of system variants and make suggestions of when each should be preferred.

4. EACL

   Document-Level Planning for Text Simplification

   Cripwell, Liam, Legrand, Joël, and Gardent, Claire

   In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics 2023

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   Most existing work on text simplification is limited to sentence-level inputs, with attempts to iteratively apply these approaches to document-level simplification failing to coherently preserve the discourse structure of the document. We hypothesise that by providing a high-level view of the target document, a simplification plan might help to guide generation. Building upon previous work on controlled, sentence-level simplification, we view a plan as a sequence of labels, each describing one of four sentence-level simplification operations (copy, rephrase, split, or delete). We propose a planning model that labels each sentence in the input document while considering both its context (a window of surrounding sentences) and its internal structure (a token-level representation). Experiments on two simplification benchmarks (Newsela-auto and Wiki-auto) show that our model outperforms strong baselines both on the planning task and when used to guide document-level simplification models.

2022

1. NAACL

   Controllable Sentence Simplification via Operation Classification

   Cripwell, Liam, Legrand, Joël, and Gardent, Claire

   In Findings of the Association for Computational Linguistics: NAACL 2022 2022

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   Different types of transformations have been used to model sentence simplification ranging from mainly local operations such as phrasal or lexical rewriting, deletion and re-ordering to the more global affecting the whole input sentence such as sentence rephrasing, copying and splitting. In this paper, we propose a novel approach to sentence simplification which encompasses four global operations: whether to rephrase or copy and whether to split based on syntactic or discourse structure. We create a novel dataset that can be used to train highly accurate classification systems for these four operations. We propose a controllable-simplification model that tailors simplifications to these operations and show that it outperforms both end-to-end, non-controllable approaches and previous controllable approaches.

2021

1. EMNLP

   Discourse-Based Sentence Splitting

   Cripwell, Liam, Legrand, Joël, and Gardent, Claire

   In Findings of the Association for Computational Linguistics: EMNLP 2021 2021

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   Sentence splitting involves the segmentation of a sentence into two or more shorter sentences. It is a key component of sentence simplification, has been shown to help human comprehension and is a useful preprocessing step for NLP tasks such as summarisation and relation extraction. While several methods and datasets have been proposed for developing sentence splitting models, little attention has been paid to how sentence splitting interacts with discourse structure. In this work, we focus on cases where the input text contains a discourse connective, which we refer to as discourse-based sentence splitting. We create synthetic and organic datasets for discourse-based splitting and explore different ways of combining these datasets using different model architectures. We show that pipeline models which use discourse structure to mediate sentence splitting outperform end-to-end models in learning the various ways of expressing a discourse relation but generate text that is less grammatical; that large scale synthetic data provides a better basis for learning than smaller scale organic data; and that training on discourse-focused, rather than on general sentence splitting data provides a better basis for discourse splitting.

2017

1. Honours Thesis

   Generating Clinical Queries from Patient Narratives

   Cripwell, Liam

   2017

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2. SIGIR

   Generating Clinical Queries from Patient Narratives: A Comparison between Machines and Humans

   Koopman, Bevan, Cripwell, Liam, and Zuccon, Guido

   In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval 2017

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   This paper investigates how automated query generation methods can be used to derive effective ad-hoc queries from verbose patient narratives. In a clinical setting, automatic query generation provides a means of retrieving information relevant to a clinician, based on a patient record, but without the need for the clinician to manually author a query. Given verbose patient narratives, we evaluated a number of query reduction methods, both generic and domain specific. Comparison was made against human generated queries, both in terms of retrieval effectiveness and characteristics of human queries. Query reduction was an effective means of generating ad-hoc queries from narratives. However, human generated queries were still significantly more effective than automatically generated queries. Further improvements were possible if parameters of the query reduction methods were set on a per-query basis and a means of predicting this was developed. Under ideal conditions, automated methods can exceed humans. Effective human queries were found to contain many novel keywords not found in the narrative. Automated reduction methods may be handicapped in that they only use terms from narrative. Future work, therefore, may be directed toward better understanding effective human queries and automated query rewriting methods that attempt to model the inference of novel terms by exploiting semantic inference processes.

2016

1. HISA

   Promoting UV Exposure Awareness with Persuasive, Wearable Technologies

   Hussain, M. S., Cripwell, Liam, Berkovsky, S., and Freyne, J.

   Studies in health technology and informatics 2016

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