Thailand

lexical ambiguity

word sense disambiguation

lexical semantics

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

This Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) was to be held in Bangkok, Thailand, during August 1-6, 2021 and is now virtual on the Underline platform.

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

ACL-IJCNLP 2021

Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations in encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT can accurately capture high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting. We also perform an in-depth comparison of the two main language model based WSD strategies, namely, fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data. In fact, the simple feature extraction strategy of averaging contextualized embeddings proves robust even using only three training sentences per word sense, with minimal improvements obtained by increasing the size of this training data.

Analysis and Evaluation of Language Models for Word Sense Disambiguation

technical paper

Cross-Lingual Abstractive Summarization with Limited Parallel Resources

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

Dynamic Contextualized Word Embeddings

Lexical Semantic Change Discovery

Named Entity Recognition via Noise Aware Training Mechanism with Data Filter

EBERT: Efficient BERT Inference with Dynamic Structured Pruning

Strong and Light Baseline Models for Fact-Checking Joint Inference

TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

UserAdapter: Few-Shot User Learning in Sentiment Analysis

workshop paper

https://d3smihljt9218e.cloudfront.net/transcript/17585/subtitle/5c50bcc77690ac569baf0d961e29324e.vtt

English (automatic)

Analysis and Evaluation of Language Models for Word Sense Disambiguation

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Cross-Lingual Abstractive Summarization with Limited Parallel Resources

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