Papers by Jeremy Cole
Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour (2022.aacl-short)
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| Challenge: | Language models trained on text-only corpora have no direct access to the physical world and thus suffer from reporting bias. |
| Approach: | They investigate reporting bias from the perspective of colour in larger language models such as PaLM and GPT-3. |
| Outcome: | The proposed models outperform smaller models on the basis of colour and more closely track human judgements than smaller models. |
NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders (2023.emnlp-main)
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| Challenge: | Neural document rerankers require dedicated hardware for serving, which is costly and often not feasible. |
| Approach: | They propose a method that captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function that only requires 10-6% of . the model architecture is compatible with recent encoder-decoder and decoder-only large language models, such as T5, GPT-3 and PaLM. |
| Outcome: | The proposed model captures 86% of the gains of a Transformer cross-attention model with a lexicalized scoring function. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Not that much power: Linguistic alignment is influenced more by low-level linguistic features rather than social power (P18-1)
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| Challenge: | linguistic alignment between interlocutors of higher power is attributed to their relative social power, but studies on low-level linguistic features do not account for these factors. |
| Approach: | They characterize the effect of power on alignment with logistic regression models in two datasets and find it vanishes after controlling for low-level features such as utterance length. |
| Outcome: | The proposed model shows that the effect vanishes or is reversed after controlling for low-level features such as utterance length. |
Selectively Answering Ambiguous Questions (2023.emnlp-main)
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| Challenge: | Prior work has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown. |
| Approach: | They propose to use a sampled set of questions to calibrate answers to ambiguous questions with varying model scales. |
| Outcome: | The results show that sampling-based confidence scores help calibrate answers to relatively unambiguous questions, with more dramatic improvements on ambiguous ones. |
The Timing of Lexical Memory Retrievals in Language Production (N18-1)
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| Challenge: | In a large-scale observational study of a spoken corpus, we find that language production at a time point preceding a word is sped up or slowed down depending on activation of that word. |
| Approach: | They propose a cognitive model of fluency in which lexical memory retrievals may explain some of the variability in speech rates. |
| Outcome: | The proposed model predicts that language production is sped up or slowed down depending on activation of a word . |
Surprisal Predicts Code-Switching in Chinese-English Bilingual Text (2020.emnlp-main)
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| Challenge: | a new study examines the propensity of bilinguals to switch languages . word surprisal and word entropy are important predictors of code-switching . |
| Approach: | They propose high cognitive effort as a reason for code-switching . they use a computational model of surprisal and word entropy to model code-changing . |
| Outcome: | The proposed model shows that word surprisal, but not entropy, is a significant predictor . sentence length is also a predictor, which has been related to sentence complexity . |