Papers with computational linguistics community

9 papers
Interpretability and Analysis in Neural NLP (2020.acl-tutorials)

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Challenge: a tutorial aims to introduce the nascent field of interpretability and analysis of neural networks in NLP .
Approach: This tutorial will introduce the nascent field of interpretability and analysis of neural networks in NLP.
Outcome: This tutorial will introduce the nascent field of interpretability and analysis of neural networks in NLP.
Measuring and Modeling Language Change (N19-5)

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Challenge: This tutorial will help researchers answer questions fundamental to the social sciences and humanities .
Approach: This tutorial is designed to help researchers answer questions in the social sciences and humanities . it synthesizes recent computational techniques for handling and modeling temporal data .
Outcome: The tutorial will synthesize recent techniques for handling and modeling temporal data, such as dynamic word embeddings, and identify useful tools for social scientists and digital humanities scholars.
Everything you need to know about Multilingual LLMs: Towards fair, performant and reliable models for languages of the world (2023.acl-tutorials)

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Challenge: Responsible AI issues such as fairness, bias and toxicity will be discussed in this tutorial .
Approach: This tutorial will describe various aspects of scaling up language technologies to many of the world’s languages by describing the latest research in Massively Multilingual Language Models (MMLMs).
Outcome: This tutorial will cover various aspects of scaling up language technologies to many of the world's languages by describing the latest research in multilingual models.
CL Scholar: The ACL Anthology Knowledge Graph Miner (N18-5)

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Challenge: ACL Anthology is a repository for papers related to computational linguistics and natural language processing.
Approach: They propose to automate periodic crawling, indexing and processing of new articles . they propose to use CL Scholar to support more than 1200 natural language queries .
Outcome: The proposed system can answer three different types of natural language queries.
Learning with Limited Text Data (2022.acl-tutorials)

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Challenge: Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP .
Approach: This tutorial will provide a systematic overview of methods for learning from limited labeled data.
Outcome: This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions .
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)

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Challenge: Current NLP models heavily rely on effective representation learning algorithms.
Approach: This tutorial introduces contrastive learning and provides an introduction to the techniques.
Outcome: This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them.
ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations (2021.eacl-main)

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Challenge: Existing research has failed to account for resisting strategies employed to foil persuasion attempts.
Approach: They propose a framework for identifying resisting strategies in persuasive conversations . they instantiate a dataset comprising persuasion and negotiation conversations based on a hierarchical sequence-labelling neural architecture .
Outcome: The proposed framework is based on two persuasive conversation datasets and leverages a hierarchical sequence-labelling neural architecture to infer resisting strategies automatically.
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models (2020.acl-demos)

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Challenge: SyntaxGym is an online platform and open-source framework for targeted syntactic evaluation of neural network language models.
Approach: They propose to make targeted syntactic evaluations accessible to both experts in NLP and linguistics and reproducible across computing environments.
Outcome: The proposed framework is reproducible across computing environments and standardized following the norms of psycholinguistic experimental design.
A Survey on Cross-Lingual Summarization (2022.tacl-1)

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Challenge: Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language.
Approach: They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other .
Outcome: The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis.

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