Recent developments in active learning algorithms for NLP tasks show promising results in terms of reducing labelling complexity. 
In this paper we extend this effort to imbalanced datasets; we bridge between the active learning approach of obtaining diverse and
informative examples, and the heuristic of class balancing used in imbalanced datasets. We develop a novel tune-free weighting technique that can
be applied to various existing active learning algorithms, adding a component of class balancing. 
We compare several active learning algorithms to their modified version on multiple public datasets
and show that when the classes are imbalanced, with manual annotation effort remaining equal the modified version significantly outperforms the original both in terms of the test metric and the number of obtained minority examples. Moreover, when the imbalance is mild or non-existent (classes are completely balanced), our technique does not harm the base algorithms.

Class Balancing for Efficient Active Learning in Imbalanced Datasets

The paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works have tried to mitigate this problem with additional pre-training, either on the downstream corpus itself when it is large enough, or on a manually curated unlabeled corpus of a similar domain. In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training. We propose TADPOLE, a task adapted pre-training framework based on data selection techniques adapted from {\em Domain Adaptation}. We formulate the data selection as an anomaly detection problem that unlike existing methods works well when the downstream corpus is limited in size. It results in a scalable and efficient unsupervised technique that eliminates the need for any manual data curation. We evaluate our framework on eight tasks across four different domains: Biomedical, Computer Science, News, and Movie reviews, and compare its performance against competitive baseline techniques from the area of Domain Adaptation. Our framework outperforms all the baseline methods. On small datasets with less than $5$K training examples, we get a gain of $1.82\%$ in performance with additional pre-training for only $5\%$ steps. It also compliments some of the other techniques such as data augmentation known for boosting performance when downstream corpus is small; highest performance is achieved when data augmentation is combined with task adapted pre-training.

TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection

Linguistic annotation of natural language corpora is the backbone of supervised methods of statistical natural language processing. The Linguistic Annotation Workshop (LAW) is the annual workshop of the ACL and ELRA Special Interest Group on Annotation (SIGANN), and it provides a forum for the presentation and discussion of innovative research on all aspects of linguistic annotation, including the creation and evaluation of annotation schemes, methods for automatic and manual annotation, use and evaluation of annotation software and frameworks, representation of linguistic data and annotations, semi-supervised “human in the loop” methods of annotation, crowd-sourcing approaches, and more. As in the past, the LAW will provide a forum for annotation researchers to work towards standardization, best practices, and interoperability of annotation information and software. [**Read more**](https://sigann.github.io/LAW-XVIII-2024/)
 [**Invited Speaker**](https://sigann.github.io/LAW-XVIII-2024/invited.html) **Program Overview** 
08:50 - 09:00 Opening Remarks 
09:00 - 10:00 Long Papers 1 
10:00 - 10:30 Short Papers 1 
10:30 - 11:00 Coffee Break 
11:00 - 11:50 Invited Talk: Lilja Øvrelid 
11:50 - 12:50 Long Papers 2 
12:50 - 14:00 Lunch Break 
14:00 - 15:00 In-Person Poster Session 
15:00 - 15:30 Short Papers 2 
15:30 - 16:00 Coffee Break 
16:00 - 17:00 Long Papers 3 
17:00 - 17:10 Short Talk 
17:10 - 17:20 Closing Remarks [**Detailed Program**](https://sigann.github.io/LAW-XVIII-2024/program.html)

W14: The 18th Linguistic Annotation Workshop (LAW-XVIII)

workshop paper

Welcome to the 18th 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 has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. 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. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

EACL 2024

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.

Virtual Poster Session II: Machine Learning for NLP

poster

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Zohar Karnin

2

Presentations

Class Balancing for Efficient Active Learning in Imbalanced Datasets

TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection

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