Papers by Lisa Beinborn
Multilingual Language Models Predict Human Reading Behavior (2021.naacl-main)
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| Challenge: | Recent studies show that cognitively motivated "attention" mechanism in neural models is not a good indicator for relative importance. |
| Approach: | They compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing. |
| Outcome: | The proposed models predict reading time measures on Dutch, English, German, and Russian texts. |
Multimodal Grounding for Language Processing (C18-1)
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| Challenge: | Recent developments in multimodal processing facilitate conceptual grounding of language. |
| Approach: | They analyze multimodal processing to examine the benefits and challenges of multimodal grounding . they focus on multimodal linguistic grounding of verbs which play a crucial role in compositional power of language. |
| Outcome: | The proposed methods improve the cognitive models of human information processing and address the challenges that arise. |
Vocabulary Shapes Cross-Lingual Variation of Word-Order Learnability in Language Models (2026.acl-long)
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| Challenge: | a coarse distinction between free- and fixed-word-order languages does not explain cross-lingual variation . aaron e. smith: human languages have emerged over millennia through communicative and cognitive constraints . but, within those universally shared bounds, languages exhibit striking typological diversity . |
| Approach: | They propose to train transformer language models on a spectrum of synthetic word-order variants of natural languages. |
| Outcome: | a new study shows that word order irregularities raise model surprisal, but only weakly affects learnability . the study also shows that vocabulary structure emerges as a key driver of word-order learnability across languages. |
Dynamic Top-k Estimation Consolidates Disagreement between Feature Attribution Methods (2023.emnlp-main)
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| Challenge: | Feature attribution scores are used to explain the prediction of a text classifier to users by highlighting a k number of tokens. |
| Approach: | They propose to determine the number of optimal k tokens that should be displayed from sequential properties of attribution scores. |
| Outcome: | The proposed method is dynamic across sentences, method-agnostic, and deals with sentence length bias. |
Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing (2024.emnlp-main)
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| Challenge: | Language models rely on frequency information because they maximize the likelihood of tokens during training. |
| Approach: | They propose a method for quantifying the frequency bias of a language model by assessing sentence-level perplexity with respect to token-level frequency. |
| Outcome: | The proposed method reduces the frequency bias of a language model by inducing a syntactic prior over token representations during pre-training. |
Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze (2020.emnlp-main)
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| Challenge: | a long tradition of cognitive studies shows that the interplay between language and vision is complex. |
| Approach: | They propose an approach to image description generation where visual processing is modelled sequentially. |
| Outcome: | The proposed model exploits gaze-driven attention to produce better descriptions . it sheds light on human cognitive processes by comparing different ways of aligning gaze with language production. |
The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement (2024.lrec-main)
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| Challenge: | Existing methods for post-hoc explanations for transformer models disagree with each other . disagreement is often overlooked and the reasons for disagreement are not investigated . |
| Approach: | They propose to use a dynamic *k* approach to estimate syntactic spans to improve agreement between different methods. |
| Outcome: | The proposed method better agrees on syntactic span level, especially for the methods that agree the least with other methods. |
Relative Importance in Sentence Processing (2021.acl-short)
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| Challenge: | In natural language processing, the relative importance of words is usually interpreted with respect to a specific task. |
| Approach: | They compare the relative importance of words in English language processing by humans and neural language models by using saliency methods. |
| Outcome: | The proposed method could be used to interpret neural language models. |
Probing Multilingual BERT for Genetic and Typological Signals (2020.coling-main)
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| Challenge: | Recent cross-lingual models provide representations for about 100 languages and vary in their training objectives. |
| Approach: | They probe the layers in multilingual BERT for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations. |
| Outcome: | The proposed model is best explained by phylogenetic and worst by structural factors and correlates with published ranked lists based on linguistic approaches. |
Cross-Lingual Transfer of Cognitive Processing Complexity (2023.findings-eacl)
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| Challenge: | Recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. |
| Approach: | They propose a multilingual model that uses structural similarities between languages to facilitate cross-lingual transfer by a meaningful bias towards sentence length and cross-linguistic differences. |
| Outcome: | The proposed model can predict varied patterns for 13 languages, despite being fine-tuned only on English data. |
Analyzing Cognitive Plausibility of Subword Tokenization (2023.emnlp-main)
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| Challenge: | Existing evaluations of subword tokenization focus on engineering criteria such as compression rate . a recent study evaluated subwords for their cognitive plausibility in languages with limited vocabulary size . |
| Approach: | They propose a new evaluation paradigm that focuses on the cognitive plausibility of subword tokenization. |
| Outcome: | The proposed tokenization algorithm yields less cognitively plausible tokenization behavior and worse coverage of derivational morphemes than previous evaluations. |