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Found 20 papers, 11 papers with code
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words
The performance of sentence encoders can be significantly improved through the simple practice of fine-tuning using contrastive loss.
Unbalanced Optimal Transport for Unbalanced Word Alignment
Monolingual word alignment is crucial to model semantic interactions between sentences.
Transformer Language Models Handle Word Frequency in Prediction Head
Prediction head is a crucial component of Transformer language models.
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
Transformers are ubiquitous in wide tasks.
Norm of Word Embedding Encodes Information Gain
Distributed representations of words encode lexical semantic information, but what type of information is encoded and how?
Improving word mover's distance by leveraging self-attention matrix
The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences.
Subspace Representations for Soft Set Operations and Sentence Similarities
In the field of natural language processing (NLP), continuous vector representations are crucial for capturing the semantic meanings of individual words.
Instance-Based Neural Dependency Parsing
Interpretable rationales for model predictions are crucial in practical applications.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Transformer architecture has become ubiquitous in the natural language processing field.
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings
(Q2) Ordinary additive compositionality can be seen as an AND operation of word meanings, but it is not well understood how other operations, such as OR and NOT, can be computed by the embeddings.
Computationally Efficient Wasserstein Loss for Structured Labels
The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation, emotion analysis, and semantic segmentation.
Efficient Estimation of Influence of a Training Instance
Understanding the influence of a training instance on a neural network model leads to improving interpretability.
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Events in a narrative differ in salience: some are more important to the story than others.
Evaluation of Similarity-based Explanations
In this study, we investigated relevance metrics that can provide reasonable explanations to users.
Word Rotator's Distance
Accordingly, we propose a method that first decouples word vectors into their norm and direction, and then computes alignment-based similarity using earth mover's distance (i. e., optimal transport cost), which we refer to as word rotator's distance.
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Interpretable rationales for model predictions play a critical role in practical applications.
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness
Large-scale dialogue datasets have recently become available for training neural dialogue agents.
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing.
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
As well as deriving PMI from mutual information, we derive this new measure from the Hilbert--Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC).
Unsupervised Learning of Style-sensitive Word Vectors
This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner.
