| Challenge: | a new perspective on how readers integrate context during reading time prediction is presented . a recent study shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor. |
| Approach: | They propose a technique where they project surprisal onto the orthogonal complement of frequency. |
| Outcome: | The proposed method shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor. |
Similar Papers
The Effects of Surprisal across Languages: Results from Native and Non-native Reading (2022.findings-aacl)
Copied to clipboard
| Challenge: | Context-dependent predictive processes have been proposed as a core component of the human cognitive system. |
| Approach: | They extract surprisal estimates from mBERT and assess their predictive power on the MECO corpus, a cross-linguistic dataset of eye movement behavior in reading. |
| Outcome: | The proposed model is based on a cross-linguistic dataset of eye movement behavior in reading. |
Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension (2021.findings-acl)
Copied to clipboard
| Challenge: | a prior work using surprisal only considered within-sentence context, using n-grams, neural language models, or syntactic structure as conditioning context. |
| Approach: | They extend the surprisal approach to use broader topical context . they identify distinct patterns of neural activation for lexical surprised and topical surpresed . |
| Outcome: | The proposed method captures effects of local and topical contexts on processing . it shows that local and broad contextual cues recruit different brain regions . |
The Linearity of the Effect of Surprisal on Reading Times across Languages (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a large amount of insight into human language processing can be gleaned by studying word-by-word processing difficulty. |
| Approach: | They extend the study by examining eyetracking corpora of seven languages . they find evidence for superlinearity in some languages, but highly sensitive to language models . |
| Outcome: | The study extends existing studies on english to Danish, Dutch, English, German, Japanese, Mandarin, and Russian. |
When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages (2021.naacl-industry)
Copied to clipboard
| Challenge: | Prior-message context provides the greatest lift in Teams (chat) scenario. |
| Approach: | They compare prior-message context with email and chat messages from Microsoft Teams and Outlook. |
| Outcome: | The proposed model outperforms existing models on two of the largest commercial communication platforms: Microsoft Teams and Outlook. |
Coreference-aware Surprisal Predicts Brain Response (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that coreference resolution is a key component of language processing and has been used to manipulate variables of interest. |
| Approach: | They propose to enable the parser to process subword information that might better approximate human morphological knowledge and extend evaluation of coreference effects from self-paced reading to human brain imaging data. |
| Outcome: | The proposed model enables the parser to process subword information that might better approximate human morphological knowledge and extends evaluation of coreference effects from self-paced reading to human brain imaging data. |
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing studies have shown that larger pre-trained language models with more parameters and lower perplexity are less predictive of human reading times. |
| Approach: | They propose to use a transformer-based model with more parameters and lower perplexity to investigate why these models are less predictive of human reading times. |
| Outcome: | The results show that the larger models with more parameters and lower perplexity are less predictive of human reading times and eye-gaze durations collected during naturalistic reading. |
Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship? (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies using LLMs on psycholinguistic data have gone unverified . a growing body of research is using word-level prediction as a computational proxy . |
| Approach: | They compare morphological, morphologic, and BPE tokenization estimates with reading time data. |
| Outcome: | The proposed method could be used to evaluate morphological prediction. |
Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent studies have shown that as Transformer-based language models become larger and are trained on very large amounts of data, the fit of their surprisal estimates to naturalistic human reading times degrades. |
| Approach: | They present a series of analyses showing that word frequency is a key explanatory factor underlying these two trends. |
| Outcome: | The results show that word frequency is a key explanatory factor underlying these two trends. |
Temperature-scaling surprisal estimates improve fit to human reading times – but does it do so for the “right reasons”? (2024.acl-long)
Copied to clipboard
| Challenge: | a wide body of evidence shows that human language processing difficulty is predicted by the information-theoretic measure surprisal, a word’s negative log probability in context. |
| Approach: | They propose to use large language models to predict the surprisal of a word's negative log probability in context to test their predictive power. |
| Outcome: | The proposed model can be significantly more accurate than humans because it has more data. |
Towards a Similarity-adjusted Surprisal Theory (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have shown that surprisal theory ignores the possibility of similarity between words and treats them as distinct entities. |
| Approach: | They propose a new measure of comprehension effort called information value that accounts for communicative equivalences between possible continuations. |
| Outcome: | The proposed measure of comprehension effort is based on the diversity index of the diversity of communicative units. |