Ranking & Metrics
Impact Score is a novel metric devised to rank conferences based on the number of contributing the best scientists in addition to the h-index estimated from the scientific papers published by the best scientists. See more details on our methodology page.
Research Impact Score:0.60
Contributing Best Scientists:4
H5-index:
Papers published by Best Scientists5
Research Ranking (Computer Science)8
Research Ranking (Social Sciences and Humanities)15
Research Ranking (Social Sciences and Humanities)7
Research Ranking (Computer Science)8
Conference Call for Papers
ACL 2022 aims to have a broad technical program. Relevant topics for the conference include, but are not limited to, the following areas (in alphabetical order):
Computational Social Science and Cultural Analytics
Dialogue and Interactive Systems
Discourse and Pragmatics
Ethics and NLP
Generation
Information Extraction
Information Retrieval and Text Mining
Interpretability and Analysis of Models for NLP
Language Grounding to Vision, Robotics and Beyond
Linguistic Theories, Cognitive Modeling, and Psycholinguistics
Machine Learning for NLP
Machine Translation and Multilinguality
NLP Applications
Phonology, Morphology, and Word Segmentation
Question Answering
Resources and Evaluation
Semantics: Lexical
Semantics: Sentence-level Semantics, Textual Inference, and Other Areas
Sentiment Analysis, Stylistic Analysis, and Argument Mining
Speech and Multimodality
Summarization
Syntax: Tagging, Chunking and Parsing
Theme: “Language Diversity: from Low-Resource to Endangered Languages”
Overview
Top Research Topics at Meeting of the Association for Computational Linguistics?
Artificial intelligence (71.69%)
Natural language processing (58.80%)
Task (project management) (18.09%)
The conference generally zeroes in on subjects such as Artificial intelligence, Natural language processing, Task (project management), Machine learning and Word (computer architecture).
The event dives deep in exploring the relationship between the study of Artificial intelligence and Speech recognition.
It focuses on Natural language processing but the discussions also offer insight into other areas such as Context (language use), SemEval and Information retrieval.
The Task (project management) study tackled is a key component of adjacent topics in the area of Domain (software engineering).
Some problems in Machine translation that were presented in the event overlapped with concepts under Translation (geometry) and Phrase.
The work on Parsing addressed in the event expands to the thematically related Grammar.
What are the most cited papers published at the conference?
Bleu: a Method for Automatic Evaluation of Machine Translation (14079 citations)
ROUGE: A Package for Automatic Evaluation of Summaries (5219 citations)
The Stanford CoreNLP Natural Language Processing Toolkit (5001 citations)
Research areas of the most cited articles at Meeting of the Association for Computational Linguistics:
Artificial intelligence, Natural language processing, Task (project management), Machine learning and Parsing are the main subjects of interest in the conference publications.
Most of the works presented in the conference articles deal with Artificial intelligence but they intersect with the subject of Speech recognition.
Issues in Natural language processing were discussed in the most cited publications, taking into consideration concepts from other disciplines like Context (language use) and Information retrieval.
What topics the last edition of the conference is best known for?
Artificial intelligence
Law
Natural language processing
The previous edition focused in particular on these issues:
The conference mainly deals with areas of study such as Artificial intelligence, Natural language processing, Task (project management), Language model and Machine learning.
The Artificial intelligence study featured in it draws parallels with the field of Context (language use).
The Natural language processing study featured in the conference draws connections with the study of Domain (software engineering).
While Task (project management) is the focus of the event, it also provided insights into the studies of F1 score, Information retrieval and Test set.
Most of the Machine learning studies addressed also intersect with Robustness (computer science).
Most of the works presented in Meeting of the Association for Computational Linguistics deals with Machine translation but it intersects with the subject of Translation (geometry).
The most cited articles from the last conference are:
Making Pre-trained Language Models Better Few-shot Learners (75 citations)
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic (36 citations)
SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense (31 citations)
Papers citation over time
A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
Research.com
Top authors and change over time
The top authors publishing at Meeting of the Association for Computational Linguistics (based on the number of publications) are:
Ming Zhou (80 papers) published 8 papers at the last edition, 4 less than at the previous edition,
Eduard Hovy (61 papers) published 6 papers at the last edition, 3 more than at the previous edition,
Noah A. Smith (60 papers) published 5 papers at the last edition, 3 less than at the previous edition,
Jun'ichi Tsujii (59 papers) absent at the last edition,
Christopher D. Manning (58 papers) published 1 paper at the last edition, 3 less than at the previous edition.
The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.
Research.com
Top affiliations and change over time
Only papers with recognized affiliations are considered
The top affiliations publishing at Meeting of the Association for Computational Linguistics (based on the number of publications) are:
Microsoft (497 papers) published 89 papers at the last edition, 19 more than at the previous edition,
Carnegie Mellon University (459 papers) published 52 papers at the last edition, 4 less than at the previous edition,
Google (284 papers) published 52 papers at the last edition, 6 less than at the previous edition,
University of Edinburgh (277 papers) published 16 papers at the last edition, 8 less than at the previous edition,
IBM (260 papers) published 32 papers at the last edition, 1 less than at the previous edition.
The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.
Research.com
Publication chance based on affiliation
The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
Research.com
During the most recent 2021 edition, 6.21% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 29.78% were posted by at least one author from the top 10 institutions publishing at the conference. Another 14.12% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.10% of all publications and 40.00% were from other institutions.
Returning Authors Index
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.
Research.com
Returning Institution Index
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Research.com
The experience to innovation index
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
