Taiwan

Conversational AI systems can engage in unsafe behaviour when handling users&#39; medical queries that may have severe consequences and could even lead to deaths.
Systems therefore need to be capable of both recognising the seriousness of medical inputs and producing responses with appropriate levels of risk. 
We create a corpus of human written English language medical queries and the responses of different types of systems. We label these with both crowdsourced and expert annotations.
While individual crowdworkers may be unreliable at grading the seriousness of the prompts, their aggregated labels tend to agree with professional opinion to a greater extent on identifying the medical queries and recognising the risk types posed by the responses.Results of classification experiments suggest that, while these tasks can be automated, caution should be exercised, as errors can potentially be very serious.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.30/)

IJCNLP-AACL 2022

Risk-graded Safety for Handling Medical Queries in Conversational AI

safety

medical

conversational ai

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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EACL 2026

Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Conversational AI systems can engage in unsafe behaviour when handling users' medical queries that may have severe consequences and could even lead to deaths.
Systems therefore need to be capable of both recognising the seriousness of medical inputs and producing responses with appropriate levels of risk. 
We create a corpus of human written English language medical queries and the responses of different types of systems. We label these with both crowdsourced and expert annotations.
While individual crowdworkers may be unreliable at grading the seriousness of the prompts, their aggregated labels tend to agree with professional opinion to a greater extent on identifying the medical queries and recognising the risk types posed by the responses.Results of classification experiments suggest that, while these tasks can be automated, caution should be exercised, as errors can potentially be very serious.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.30/)

poster

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

A registration is required to access this event. Access to the event site will be granted to all registered attendees on **November 16th**. If you have not already done so, please register using the link below:
 
 https://conference.iis.sinica.edu.tw/servlet/Register?ConferenceID=411

Registration Is Required

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.44/)

Modeling Document-level Temporal Structures for Building Temporal Dependency Graph

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.23/)

Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics

To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separate events due to the hallucination problem, and additionally the generated event sequences are often hard to control due to the end-to-end nature of the models. To address these challenges, we propose NGEP, an novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches, considering both the performance of event sequence generation and the effectiveness on the downstream task of story generation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.24/)

NGEP: A Graph-based Event Planning Framework for Story Generation

Pre-trained language models such as KoBART often fail to generate perfect interrogative sentences when they are applied to Korean question generation. 
This is mainly due to the fact that the language models are trained with declarative sentences, but not with interrogative sentences. 
Therefore, this paper proposes a novel post-training of KoBART to enhance it for Korean question generation. 
The enhancement of KoBART is accomplished in three ways: 
(i) introduction of question infilling objective to KoBART to enforce it to focus more on the structure of interrogative sentences, 
(ii) augmentation of training data for question generation with another MRC data from AI-Hub to cope with the lack of training instances for post-training, 
(iii) introduction of Korean spacing objective to make KoBART understand the linguistic features of Korean. 
Since there is no standard data set for Korean question generation, this paper also proposes KorQuAD-QG, 
a new data set for this task, to verify the performance of the proposed post-training. 
Our code are publicly available at https://github.com/gminipark/post_training_qg.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.26/)

Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator

A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, labeled data is often scarce, particularly in multilingual settings. Large Language Models (LLMs) excel at SP given only a few examples, however LLMs are unsuitable for runtime systems which require low latency. In this work, we propose CLASP, a simple method to improve low-resource SP for moderate-sized models: we generate synthetic data from AlexaTM 20B to augment the training set for a model 40x smaller (500M parameters). We evaluate on two datasets in low-resource settings: English PIZZA, containing either 348 or 16 real examples, and mTOP cross-lingual zero-shot, where training data is available only in English, and the model must generalize to four new languages. On both datasets, we show significant improvements over strong baseline methods.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.56/)

CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.55/)

An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.14/)

An Effective Post-training Embedding Binarization Approach for Fast Online Top-K Passage Matching

Graph-based text representation focuses on how text documents are represented as graphs for exploiting dependency information between tokens and documents within a corpus. Despite the increasing interest in graph representation learning, there is limited research in exploring new ways for graph-based text representation, which is important in downstream natural language processing tasks. In this paper, we first propose a new heterogeneous word-character text graph that combines word. and character n-gram nodes together with document nodes, allowing us to better learn dependencies among these entities. Additionally, we propose two new graph-based neural models, WCTextGCN and WCTextGAT, for modeling our proposed text graph. Extensive experiments in text classification and automatic
text summarization benchmarks demonstrate that our proposed models consistently outperform competitive baselines and state-of-the-art graph-based models.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.29/)

Improving Graph-Based Text Representations with Character and Word Level N-grams

Machine learning systems have shown impressive performance across a range of natural language tasks. However, it has been hypothesized that these systems are prone to learning spurious correlations that may be present in the training data. Though these correlations will not impact in-domain performance, they are unlikely to generalize well to out-of-domain data, limiting the applicability of systems. This work examines this phenomenon on text classification tasks. Rather than artificially injecting features into the data, we demonstrate that real spurious correlations can be exploited by current state-of-the-art deep-learning systems. Specifically, we show that even when only 'stop' words are available at the input stage, it is possible to predict the class significantly better than random. Though it is shown that these stop words are not required for good in-domain performance, they can degrade the ability of the system to generalize well to out-of-domain data.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.11/)

Analyzing Biases to Spurious Correlations in Text Classification Tasks

Language models (LMs) trained on raw texts have no direct access to the physical world. Gordon and Van Durme (2013) point out that LMs can thus suffer from reporting bias: texts rarely report on common facts, instead focusing on the unusual aspects of a situation. If LMs are only trained on text corpora and naively memorise local co-occurrence statistics, they thus naturally would learn a biased view of the physical world. While prior studies have repeatedly verified that LMs of smaller scales (e.g., RoBERTa, GPT-2) amplify reporting bias, it remains unknown whether such trends continue when models are scaled up. We investigate reporting bias from the perspective of colour in larger language models (LLMs) such as PaLM and GPT-3. Specifically, we query LLMs for the typical colour of objects, which is one simple type of perceptually grounded physical common sense. Surprisingly, we find that LLMs significantly outperform smaller LMs in determining an object's typical colour and more closely track human judgments, instead of overfitting to surface patterns stored in texts. This suggests that very large models of language alone are able to overcome certain types of reporting bias that are characterized by local co-occurrences.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.27/)


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Modeling Document-level Temporal Structures for Building Temporal Dependency Graph

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