Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models. Recent studies found that superficial factors such as word length can distort human interpretation of the communicated saliency scores. We conduct a user study to investigate how the marking of a word's *neighboring words* affect the explainee's perception of the word's importance in the context of a saliency explanation.We find that neighboring words have significant effects on the word's importance rating.Concretely, we identify that the influence changes based on neighboring direction (left vs. right) and a-priori linguistic and computational measures of phrases and collocations (vs. unrelated neighboring words).Our results question whether text-based saliency explanations should be continued to be communicated at word level, and inform future research on alternative saliency explanation methods.

<iframe src="https://app.sli.do/event/kgQkh7cPzt9VEM52HG3MJu/embed/polls/bc4ba7fb-d393-4647-8161-40f02128c283" width="300" height="400"></iframe>

Neighboring Words Affect Human Interpretation of Saliency Explanations

We introduce our work submitted to SemEval-2023 Task 12: "Sentiment Analysis for Low-resource African Languages using Twitter dataset". In our system, we leverage adaptive pretraining on African texts and study transfer learning with source language selection. Our system wins 2 of 3 subtasks and performs best in 7 out of 14 languages in the shared task.

NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis

Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the domain gap between the pre-training data and the downstream task. In this work, we bridge this gap with a novel and lightweight prompting methodology called SwitchPrompt for the adaptation of language models trained on datasets from the general domain to diverse low-resource domains. Using domain-specific keywords with a trainable gated prompt, SwitchPrompt offers domain-oriented prompting, that is, effective guidance on the target domains for general-domain language models. Our few-shot experiments on three text classification benchmarks demonstrate the efficacy of the general-domain pre-trained language models when used with SwitchPrompt. They often even outperform their domain-specific counterparts trained with baseline state-of-the-art prompting methods by up to 10.7\% performance increase in accuracy. This result indicates that SwitchPrompt effectively reduces the need for domain-specific language model pre-training.

SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains

The detection and normalization of temporal expressions is an important task and preprocessing step for many applications. 
However, prior work on normalization is rule-based, which severely limits the applicability in real-world multilingual settings, due to the costly creation of new rules. 
We propose a novel neural method for normalizing temporal expressions based on masked language modeling. 
Our multilingual method outperforms prior rule-based systems in many languages, and in particular, for low-resource languages with performance improvements of up to 33 F1 on average compared to the state of the art.

Multilingual Normalization of Temporal Expressions with Masked Language Models

Entity linking disambiguates mentions by mapping them to entities in a knowledge graph (KG). One important question in today’s research is how to extend neural entity linking systems to new domains. In this paper, we aim at a system that enables linking mentions to entities from a general-domain KG and a domain-specific KG at the same time. In particular, we represent the entities of different KGs in a joint vector space and address the questions of which data is best suited for creating and fine-tuning that space, and whether fine-tuning harms performance on the general domain. We find that a combination of data from both the general and the special domain is most helpful. The first is especially necessary for avoiding performance loss on the general domain. While additional supervision on entities that appear in both KGs performs best in an intrinsic evaluation of the vector space, it has less impact on the downstream task of entity linking.

A Study on Entity Linking Across Domains":" Which Data is Best for Fine-Tuning?

Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a growing body of work to improve the performance in low-resource settings. Motivated by the recent fundamental changes towards neural models and the popular pre-train and fine-tune paradigm, we survey promising approaches for low-resource natural language processing. After a discussion about the different dimensions of data availability, we give a structured overview of methods that enable learning when training data is sparse. This includes mechanisms to create additional labeled data like data augmentation and distant supervision as well as transfer learning settings that reduce the need for target supervision. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific low-resource setting. Further key aspects of this work are to highlight open issues and to outline promising directions for future research.

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

Findings papers without presentation slot

poster

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

To view Day 2 Live Session Recording click [**here**](https://underline.io/events/395/sessions?eventSessionId=15300&searchGroup=lecture) SemEval is a series of international natural language processing (NLP) research workshops whose mission is to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a range of increasingly challenging problems in natural language semantics. Each year's workshop features a collection of shared tasks in which computational semantic analysis systems designed by different teams are presented and compared.

SemEval is sponsored by the SIGLEX Special Interest Group on the Lexicon of the Association for Computational Linguistics. For more information on SemEval and its history, please see http://en.wikipedia.org/wiki/SemEval.
 For more details visit our workshop website: https://semeval.github.io/SemEval2023/
#### [Visit GatherTown](https://app.gather.town/app/5OscLswEEuYf2Psb/ACL2023)

W1: The 17th International Workshop on Semantic Evaluation (SemEval)_Day 1

workshop paper

Poster Session 5

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Poster Session 3

**Organizers:** Spandana Gella, He He, Burcu Can, Maximilian Mozes, Eleonora Giunchiglia, Sewon Min,
Samuel Cahyawijaya, Xiang Lorraine Li and Bodhisattwa Prasad Majumder **Description:** The 7th Workshop on Representation Learning for NLP (RepL4NLP 2022) will be hosted by ACL 2022 and held on
26 May 2022. The workshop is being organised by Spandana Gella, He He, Burcu Can, Maximilian Mozes, Eleonora
Giunchiglia, Sewon Min, Samuel Cahyawijaya, Xiang Lorraine Li and Bodhisattwa Prasad Majumder; and advised by
Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Chris Dyer and Laura Rimell. The workshop
is organised by the ACL Special Interest Group on Representation Learning (SIGREP). The 7th Workshop on Representation Learning for NLP aims to continue the success of the Repl4NLP workshop series, with the 1st Workshop
on Representation Learning for NLP having received about 50 submissions and over 250 attendees – the second most
attended collocated event at ACL’16 after WMT). The workshop was introduced as a synthesis of several years of independent ACL workshops focusing on vector space models of meaning, compositionality, and the application of deep
neural networks and spectral methods to NLP. It provides a forum for discussing recent advances on these topics, as well
as future research directions in linguistically motivated vector-based models in NLP. The workshop will take place in a
hybrid setting, and, as in previous years, feature interdisciplinary keynotes, paper presentations, posters, as well as a panel
discussion. 
**Please visit our [website](https://sites.google.com/view/repl4nlp2022/)**

(RepL4NLP)        7th Workshop on Representation Learning for NLP

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

**Session Chair: ** Paul Michel

7E-Oral: Machine Learning for NLP: Classification and Structured Prediction Models

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

**Whova App** 
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**Conference Structure**
https://2021.naacl.org/blog/conference-structure/

**Walkthrough video of how to NAACL 2021** 

Please take a moment to view this video explaining how to navigate the platform, attend sessions network with other attendees. 


<figure class="video_container">
 <iframe src="https://screencast-o-matic.com/watch/crhwbGVh3vx?v=6&ff=1&title=0&controls=1" width=640 height=350 frameborder="0" allowfullscreen="true"> </iframe>
</figure>

Heike Adel

6

10

SHORT BIO

Presentations

Neighboring Words Affect Human Interpretation of Saliency Explanations

NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis

SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains

Multilingual Normalization of Temporal Expressions with Masked Language Models

A Study on Entity Linking Across Domains":" Which Data is Best for Fine-Tuning?

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

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PRESENTATIONS

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