Andrew Caines
Biographic Data
| ID | 8614899 |
|---|---|
| NAME | Andrew Caines |
| GIVEN NAMES | Andrew |
| FAMILY NAME | Caines |
| SIGNATURE | CAINES A |
| AFFILIATIONS | University of Cambridge |
| ORCID | 0000-0001-9647-4902 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Towards Equitable, Diverse and Inclusive Language Models in AI-enhanced Education Technology for Language Learners
Artificial intelligence (AI) plays an important and growing part in education technology for language learning and assessment. The role of AI ranges from automated assessment to task selection, and automated feedback to content creation. Recent trends in language-related AI – ‘natural language processing&s;, as it is known – involve large language models which are capable of generating fairly coherent and grammatical texts, holding chat conversat…
Corpus-Based Reflective Practice to Support Chatroom Teaching Practice
Teaching practice has long been considered a fundamental and integral part of any teacher education programme, but also very demanding for novice teachers when they are confronted with the reality of the classroom, for the first time in many cases. Teacher educators aim to allow student teachers to experience practice opportunities reflective of their many potential real-world future teaching scenarios, including, for example, teaching online thr…
Word segmentation from transcriptions of child-directed speech using lexical and sub-lexical cues
We compare two frameworks for the segmentation of words in child-directed speech, PHOCUS and MULTICUE. PHOCUS is driven by lexical recognition, whereas MULTICUE combines sub-lexical properties to make boundary decisions, representing differing views of speech processing. We replicate these frameworks, perform novel benchmarking and confirm that both achieve competitive results. We develop a new framework for segmentation, the DYnamic Programming …
Building natural language processing tools for Runyakitara
This paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, befo…
The cross-linguistic performance of word segmentation models over time
We select three word segmentation models with psycholinguistic foundations – transitional probabilities, the diphone-based segmenter, and PUDDLE – which track phoneme co-occurrence and positional frequencies in input strings, and in the case of PUDDLE build lexical and diphone inventories. The models are evaluated on caregiver utterances in 132 CHILDES corpora representing 28 languages and 11.9 m words. PUDDLE shows the best performance overall, …
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The cross-linguistic performance of word segmentation models over time
We select three word segmentation models with psycholinguistic foundations – transitional probabilities, the diphone-based segmenter, and PUDDLE – which track phoneme co-occurrence and positional frequencies in input strings, and in the case of PUDDLE build lexical and diphone inventories. The models are evaluated on caregiver utterances in 132 CHILDES corpora representing 28 languages and 11.9 m words. PUDDLE shows the best performance overall, …
Building natural language processing tools for Runyakitara
This paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, befo…
Towards Equitable, Diverse and Inclusive Language Models in AI-enhanced Education Technology for Language Learners
Artificial intelligence (AI) plays an important and growing part in education technology for language learning and assessment. The role of AI ranges from automated assessment to task selection, and automated feedback to content creation. Recent trends in language-related AI – ‘natural language processing&s;, as it is known – involve large language models which are capable of generating fairly coherent and grammatical texts, holding chat conversat…
Corpus-Based Reflective Practice to Support Chatroom Teaching Practice
Teaching practice has long been considered a fundamental and integral part of any teacher education programme, but also very demanding for novice teachers when they are confronted with the reality of the classroom, for the first time in many cases. Teacher educators aim to allow student teachers to experience practice opportunities reflective of their many potential real-world future teaching scenarios, including, for example, teaching online thr…
Word segmentation from transcriptions of child-directed speech using lexical and sub-lexical cues
We compare two frameworks for the segmentation of words in child-directed speech, PHOCUS and MULTICUE. PHOCUS is driven by lexical recognition, whereas MULTICUE combines sub-lexical properties to make boundary decisions, representing differing views of speech processing. We replicate these frameworks, perform novel benchmarking and confirm that both achieve competitive results. We develop a new framework for segmentation, the DYnamic Programming …
Computer Science (4 works) · Linguistics (4 works) · Natural language processing (4 works) · Artificial Intelligence (3 works) · Psychology (3 works) · Grammar (2 works) · Language Development and Disorders (2 works) · Natural Language Processing Techniques (2 works) · Segmentation (2 works) · Speech segmentation (2 works)