Papers with computational linguistics community
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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Mayank Singh, Pradeep Dogga, Sohan Patro, Dhiraj Barnwal, Ritam Dutt, Rajarshi Haldar, Pawan Goyal, Animesh Mukherjee
| 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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Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, Carolyn Rose
| 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. |