Publications associées à Word Embeddings for Natural Language Processing

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Interpreting Language Models Through Knowledge Graph Extraction

Martin Jaggi, Vinitra Swamy, Angeliki Romanou

Transformer-based language models trained on large text corpora have enjoyed immense popularity in the natural language processing community and are commonly used as a starting point for downstream tasks. While these models are undeniably useful, it is a c ...

2021

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Karl Aberer, Rémi Philippe Lebret, Mohammadreza Banaei

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression of thes ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Self-Supervised Neural Topic Modeling

Martin Jaggi

Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semanti ...

Assoc Computational Linguistics-Acl2021

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

James Henderson, Alireza Mohammadshahi

We describe the DCU-EPFL submission to the IWPT 2021 Parsing Shared Task: From Raw Text to Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Lightweight Cross-Lingual Sentence Representation Learning

Martin Jaggi, Prakhar Gupta, Zhuoyuan Mao

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Low-Rank Subspaces for Unsupervised Entity Linking

Robert West, Akhil Arora, Alberto Garcia Duran

Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains. We propose a light-weight and scalable entity ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

Pearl Pu Faltings, Yubo Xie, Junze Li

Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the se ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

The Unstoppable Rise of Computational Linguistics in Deep Learning

James Henderson

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...

Association for Computational Linguistics2020

Word Embeddings for Natural Language Processing

Graph Chatbot

Chattez avec Graph Search

Interpreting Language Models Through Knowledge Graph Extraction

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Self-Supervised Neural Topic Modeling

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

Lightweight Cross-Lingual Sentence Representation Learning

Low-Rank Subspaces for Unsupervised Entity Linking

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

The Unstoppable Rise of Computational Linguistics in Deep Learning

Concept Discovery for The Interpretation of Landscape Scenicness

RSVQA: Visual Question Answering for Remote Sensing Data

RSVQA: Visual Question Answering for Remote Sensing Data

Interpreting Language Models Through Knowledge Graph Extraction

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

Concept Discovery for The Interpretation of Landscape Scenicness

The Unstoppable Rise of Computational Linguistics in Deep Learning

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Lightweight Cross-Lingual Sentence Representation Learning

Self-Supervised Neural Topic Modeling

Low-Rank Subspaces for Unsupervised Entity Linking

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition