United Arab Emirates

We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five human annotators, respectively. We demonstrate that conciseness is a difficult task for which zero-shot setups with large neural language models often do not perform well. Given the limitations of these approaches, we propose a synthetic data generation method based on round-trip translations. Using this data to either train Transformers from scratch or fine-tune T5 models yields our strongest baselines that can be further improved by fine-tuning on an artificial conciseness dataset that we derived from multi-annotator machine translation test sets.

EMNLP 2022

Conciseness: An Overlooked Language Task

text to text

conciseness

writing style

sentence compression

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

workshop paper

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Academic writing should be concise as concise sentences better keep the readers' attention and convey meaning clearly. Writing concisely is challenging, for writers often struggle to revise their drafts. We introduce and formulate revising for concision as a natural language processing task at the sentence level. Revising for concision requires algorithms to use only necessary words to rewrite a sentence while preserving its meaning. The revised sentence should be evaluated according to its word choice, sentence structure, and organization. The revised sentence also needs to fulfil semantic retention and syntactic soundness. To aide these efforts, we curate and make available a benchmark parallel dataset that can depict revising for concision. The dataset contains 536 pairs of sentences before and after revising, and all pairs are collected from college writing centres. We also present and evaluate the approaches to this problem, which may assist researchers in this area.

Revision for Concision: A Constrained Paraphrase Generation Task

In Neural Machine Translation (NMT) systems, there is generally little control over the lexicon of the output. Consequently, the translated output may be too difficult for certain audiences.  For example, for people with limited knowledge of the language, vocabulary is a major impediment to understanding a text. In this work, we build a complexity-controllable NMT for English-to-Japanese translations. More particularly, we aim to modulate the difficulty of the translation in terms of not only the vocabulary but also the use of kanji. For achieving this, we follow a sentence-tagging approach to influence the output.Controlling Japanese Machine Translation Output by Using JLPT Vocabulary Levels.

Controlling Japanese Machine Translation Output by Using JLPT Vocabulary Levels

This study presents a lexical simplification (LS) methodology for foreign language (FL) learning purposes, a barely explored area of automatic text simplification (TS). The method, targeted at Spanish as a foreign language (SFL), includes a customised complex word identification (CWI) classifier and generates substitutions based on masked language modelling. Performance is calculated on a custom dataset by means of a new, pedagogically-oriented evaluation. With 43\% of the top simplifications being found suitable, the method shows potential for simplifying sentences to be used in FL learning activities. The evaluation also suggests that, though still crucial, meaning preservation is not always a prerequisite for successful LS. To arrive at grammatically correct and more idiomatic simplifications, future research could study the integration of association measures based on co-occurrence data.

Lexical Simplification in Foreign Language Learning: Creating Pedagogically Suitable Simplified Example Sentences

Eye movements are known to reflect cognitive processes in reading, and psychological reading research has shown that eye gaze patterns differ between readers with and without dyslexia. In recent years, researchers have attempted to classify readers with dyslexia based on their eye movements using Support Vector Machines (SVMs). However, these approaches (i) are based on highly aggregated features averaged over all words read by a participant, thus disregarding the sequential nature of the eye movements, and (ii) do not consider the linguistic stimulus and its interaction with the reader's eye movements. In the present work, we propose two simple sequence models that process eye movements on the entire stimulus without the need of aggregating features across the sentence. Additionally, we incorporate the linguistic stimulus into the model in two ways---contextualized word embeddings and manually extracted linguistic features. The models are evaluated on a Mandarin Chinese dataset containing eye movements from children with and without dyslexia. Our results show that (i) even for a logographic script such as Chinese, sequence models are able to classify dyslexia on eye gaze sequences, reaching state-of-the-art performance, and (ii) incorporating the linguistic stimulus does not help to improve classification performance.

Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models

Research has explored the use of automatic text simplification (ATS), which consists of techniques to make text simpler to read, to provide reading assistance to Deaf and Hard-of-hearing (DHH) adults with various literacy levels. Prior work in this area has identified interest in and benefits from ATS-based reading assistance tools. However, no prior work on ATS has gathered judgements from DHH adults as to what constitutes complex text. Thus, following approaches in prior NLP work, this paper contributes new word-complexity judgements from 11 DHH adults on a dataset of 15,000 English words that had been previously annotated by L2 speakers, which we also augmented to include automatic annotations of linguistic characteristics of the words. Additionally, we conduct a supplementary analysis of the interaction effect between the linguistic characteristics of the words and the groups of annotators. This analysis highlights the importance of collecting judgements from DHH adults for training ATS systems, as it revealed statistically significant interaction effects for nearly all of the linguistic characteristics of the words.

A Dataset of Word-Complexity Judgements from Deaf and Hard-of-Hearing Adults for Text Simplification

State-of-the-art text simplification (TS) systems adopt end-to-end neural network models to directly generate the simplified version of the input text, and usually function as a blackbox. Moreover, TS is usually treated as an all-purpose generic task under the assumption of homogeneity, where the same simplification is suitable for all. In recent years, however, there has been increasing recognition of the need to adapt the simplification techniques to the specific needs of different target groups. In this work, we aim to advance current research on explainable and controllable TS in two ways: First, building on recently proposed work to increase the transparency of TS systems (Garbacea et al., 2020), we use a large set of (psycho-)linguistic features in combination with pre-trained language models to improve explainable complexity prediction. Second, based on the results of this preliminary task, we extend a state-of-the-art Seq2Seq TS model, ACCESS (Martin et al., 2020), to enable explicit control of ten attributes. The results of experiments show (1) that our approach improves the performance of state-of-the-art models for predicting explainable complexity and (2) that explicitly conditioning the Seq2Seq model on ten attributes leads to a significant improvement in performance in both within-domain and out-of-domain settings.

(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification

Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.

Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification

Recent work on text simplification has focused on the use of control tokens to further the state of the art. However, it is not easy to further improve without an in-depth comprehension of the mechanisms underlying control tokens. One unexplored factor is the tokenisation strategy, which we also explore. In this paper, we (1) reimplemented ACCESS, (2) explored the effects of varying control tokens, (3) tested the influences of different tokenisation strategies, and (4) demonstrated how separate control tokens affect performance. We show variations of performance in the four control tokens separately. We also uncover how the design of control tokens could influence the performance and propose some suggestions for designing control tokens, which also reaches into other controllable text generation tasks.

An Investigation into the Effect of Control Tokens on Text Simplification

Text simplification is a task to reduce the complexity of a text while retain its original meaning. It can facilitate people with low-literacy skills or language impairments, such as children and individuals with dyslexia and aphasia, to read and understand complicated materials. Normally, substitution, deletion, reordering, and splitting are considered as four core operations for performing text simplification. Thus an ideal model should be capable of executing these operations appropriately to simplify a text. However, by examining the degree that each operation is exerted in different datasets, we observe that there is a salient discrepancy between the human annotation and existing training data that is widely used for training simplification models. To alleviate this discrepancy, we propose an unsupervised data construction method that distills each simplifying operation into data via different automatic data enhancement measures. The empirical results demonstrate that the resulting dataset SimSim can support models to achieve better performance by performing all operations properly.

Divide-and-Conquer Text Simplification by Scalable Data Enhancement

In the past few years, the field of text simplification has been dominated by supervised learning approaches thanks to the appearance of large parallel datasets such as Wikilarge and Newsela. However, these datasets suffer from sentence pairs with factuality errors which compromise the models' performance. So, we proposed a model-independent factuality error detection mechanism, considering bad simplification and bad alignment, to refine the Wikilarge dataset through reducing the weight of these samples during training. We demonstrated that this approach improved the performance of the state-of-the-art text simplification model TST5 by an FKGL reduction of 0.33 and 0.29 on the TurkCorpus and ASSET testing datasets respectively. Our study illustrates the impact of erroneous samples in TS datasets and highlights the need for automatic methods to improve their quality.

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Revision for Concision: A Constrained Paraphrase Generation Task

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