🧑‍🔬 Tyler Loakman

🏛️ Affiliations

🎓University of SheffieldSheffield · United Kingdom2021–2026 · 27 papersEducation 2021–2025 · Speech and Language Technologies PhD, CDT in Speech and Language TechnologiesEducation 2021–2025 · SLT Leadership PGDip, CDT in Speech and Language Technologiescurrent🎓University of ManchesterManchester · United Kingdom2026Employment since 2026 · Research Associate, Computer Science

🏛️Defined.aiSeattle · United States2021Employment 2021 · Freelance Phonetician (Remote), Data Crowdsourcing

🎓University of YorkYork · United Kingdom2020–2021Education 2020–2021 · Forensic Speech Science MSc - Distinction, Language & Linguistic Science

🏛️The University of HuddersfieldHuddersfield · United Kingdom2017–2020Education 2017–2020 · English Language & Linguistics BA (Hons) - 1st Class, The School of Music, Humanities and MediaEmployment 2018–2019 · WYRED Project Orthographic Transcriber, The School of Music, Humanities and Media

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation (2023 · 19 citations)
   Human judgement is usually treated as the most trustworthy way to assess generated text. This position paper argues that this trust is especially fragile for humour, irony and sarcasm. How people read these forms depends heavily on who they are: their politics, gender, culture, region, background knowledge and…
2. COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026)
   Chinese alignment work lacks good preference data. Existing Chinese datasets are small, often drawn from a single source such as Zhihu, and seldom checked by training models on them. Human labelling is also too costly to scale. The authors build an annotation pipeline that uses LLMs only, with no human labelling.…

All works

5 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm GenerationFindings of the Association for Computational LinguisticsTyler Loakman, Aaron Maladry, Chenghua Lin2023 · 19 citations
2. MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language ↗Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · no summary yetShun Wang et al.2024 · 16 citations
3. Train and Constrain: Phonologically Informed Tongue Twister Generation from Topics and Paraphrases ↗Computational Linguistics · no summary yetTyler Loakman, Chen Tang, Chenghua Lin2025 · 3 citations
4. Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases ↗arXiv:2403.13901 · no summary yetTyler Loakman, Chen Tang, Chenghua Lin2024 · 2 citations
5. COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human ValuesFindings of the Association for Computational LinguisticsSiwei Wu, JinCheng Ren, Xeron Du, et al.2026

🤝 Co-authors here

8 co-authors with a page, the most papers together first.

• Aaron Maladry
• Chenghua Lin
• Jie Liu
• Shuyue Guo
• Siwei Wu
• Tianyu Zheng
• Xingwei Qu
• Yiming Liang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026, Findings of the Association for Computational Linguistics 2023
• Computational Linguistics
• Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

🔗 Cited by

Papers of our library cite their works 29 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated 2 November 2026

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