Search Results for author:
Found 24 papers, 14 papers with code
SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task
This paper presents our system used to participate in task 11 (MultiCONER) of the SemEval 2022 competition.
A Cross-lingual Comparison of Human and Model Relative Word Importance
Relative word importance is a key metric for natural language processing.
The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement
We systematically investigate the interaction between $k$ and spans and propose an improved configuration for selecting important tokens.
CLIMB: Curriculum Learning for Infant-inspired Model Building
We describe our team's contribution to the STRICT-SMALL track of the BabyLM Challenge.
Analyzing Cognitive Plausibility of Subword Tokenization
Subword tokenization has become the de-facto standard for tokenization, although comparative evaluations of subword vocabulary quality across languages are scarce.
Dynamic Top-k Estimation Consolidates Disagreement between Feature Attribution Methods
Feature attribution scores are used for explaining the prediction of a text classifier to users by highlighting a k number of tokens.
Cross-Lingual Transfer of Cognitive Processing Complexity
When humans read a text, their eye movements are influenced by the structural complexity of the input sentences.
Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition
Argument Unit Recognition and Classification aims at identifying argument units from text and classifying them as pro or against.
Relative Importance in Sentence Processing
In neural language models, gradient-based saliency methods indicate the relative importance of a token for the target objective.
Multilingual Language Models Predict Human Reading Behavior
We analyze if large language models are able to predict patterns of human reading behavior.
Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze
When speakers describe an image, they tend to look at objects before mentioning them.
Probing Multilingual BERT for Genetic and Typological Signals
We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representations.
Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing
NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text.
The Fluidity of Concept Representations in Human Brain Signals
We argue that fluid concept representations lead to more realistic models of human language processing because they better capture the ambiguity and underspecification present in natural language use.
Blackbox Meets Blackbox: Representational Similarity \& Stability Analysis of Neural Language Models and Brains
In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models.
Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains
In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models.
Semantic Drift in Multilingual Representations
We propose to conduct an adapted version of representational similarity analysis of a selected set of concepts in computational multilingual representations.
Robust Evaluation of Language-Brain Encoding Experiments
Language-brain encoding experiments evaluate the ability of language models to predict brain responses elicited by language stimuli.
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language.
A domain-agnostic approach for opinion prediction on speech
We explore a domain-agnostic approach for analyzing speech with the goal of opinion prediction.
Predicting the Difficulty of Language Proficiency Tests
Language proficiency tests are used to evaluate and compare the progress of language learners.
