Liam Cripwell

CV/Resume

I am a recent PhD graduate who has been working in the SyNaLP team of LORIA, under the supervision of Claire Gardent and Joël Legrand. My PhD research was focused on controllable and document-level approaches to text simplification.

I have broad research interests including generative NLP models, evaluation, controllability/alignment, and information retrieval. I am also passionate about applications of NLP and machine learning to accessability, medicine/health-care, and other human-facing domains.

Before my PhD I was a machine learning engineer at Notiv (now Dubber) where I worked on the implementation and deployment of machine learning products for natural language and speech applications.

I completed my bachelors and honours degrees at the Queensland University of Technology (QUT) in Brisbane, Australia. My honours thesis focused on the generation of clinical search queries and was supervised by Guido Zuccon and Bevan Koopman.

selected publications

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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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.
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.
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.
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.
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.