Indonesia

The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online. While several recent efforts have focused on detecting AI-generated text in general, these methods require enhanced reliability, given concerns about their vulnerability to simple adversarial attacks. Furthermore, due to the eccentricities of news writing, applying these detection methods for AI-generated news can produce false positives, potentially damaging the reputation of news organizations. To address these challenges, we leverage the expertise of an interdisciplinary team to develop a framework, J-Guard, capable of steering existing supervised AI text detectors for detecting AI-generated news while boosting adversarial robustness. By incorporating stylistic cues inspired by the unique journalistic attributes, J-Guard effectively distinguishes between real-world journalism and AI-generated news articles. Our experiments on news articles generated by a vast array of AI models, including ChatGPT (GPT3.5), demonstrate the effectiveness of J-Guard in enhancing detection capabilities while maintaining an average performance decrease of as low as 7% when faced with adversarial attacks.

IJCNLP-AACL 2023

J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

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### Welcome to IJCNLP-AACL 2023!
I am honored and happy to welcome you to IJCNLP-AACL 2023! The conference hosts both the 13th
International Joint Conference on Natural Language Processing (IJCNLP) and the 3rd Conference of
the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL). 
The joint conferences of IJCNLP and AACL have been organized by alternating leadership in the
Asia-Pacific region, in odd years by Asian Federation of Natural Language Processing (AFNLP) and
in even years by AACL. The conference is organized by AFNLP this year. 

Considering the current situation that we are not yet fully over the coronavirus pandemic, the
conference is offered in a hybrid mode, off-line in Nusa Dua, Bali, Indonesia, and on-line with the help
of Underline, during November 1-4, 2023. For those attending off-line, I hope that you will find time to
explore the exciting local area, which remains quite warm and pleasant at this time of the year. For
those attending on-line, I hope that the attendance is not much hampered, sometimes even enhanced
and comfortable, though the on-line arrangements have been made without ample time. 

Message from Jong C. Park, Korea Advanced Institute of Science and Technology (KAIST) 
IJCNLP-AACL 2023 General Chair 
###### [Conference Handbook](https://drive.google.com/file/d/1S2JSXa105xJxIkNBfhYmGH20Om6wARBx/view?usp=sharing)

In order to acces this event page, you need to register [here](http://www.ijcnlp-aacl2023.org/)

The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics

In Natural Language Processing (NLP) classification tasks such as topic categorisation and sentiment analysis, model generalizability is generally measured with standard metrics such as Accuracy, F-Measure, or AUC-ROC. The diversity of metrics, and the arbitrariness of their application suggest that there is no agreement within NLP on a single best metric to use. This lack suggests there has not been sufficient examination of the underlying heuristics which each metric encodes. To address this we compare several standard classification metrics with more 'exotic' metrics and demonstrate that a random-guess normalised Informedness metric is a parsimonious baseline for task performance. To show how important the choice of metric is, we perform extensive experiments on a wide range of NLP tasks including a synthetic scenario, natural language understanding, question answering and machine translation. Across these tasks we use a superset of metrics to rank models and find that Informedness best captures the ideal model characteristics. Finally, we release a Python implementation of Informedness following the SciKitLearn classifier format.

We Need to Talk About Classification Evaluation Metrics in NLP

Large language models (LLMs) have shown remarkable performance on a variety of multilingual NLP tasks. Code-switching is one of the most convenient styles of communication in multilingual communities. It is known to present several challenges to the existing language models and task-specific models. In this paper, we evaluate the capability of multilingual LLMs for the code-switched machine translation (CSMT) task in traditional and novel settings and present our insights. We observe that ChatGPT outperforms other LLMs and shows competitive performance to the supervised fine-tuned models. Though promising, ChatGPT shows major limitations, such as high gender bias, stereotypes, and factual inconsistencies. It further demands a multi-dimensional large-scale evaluation of the multilingual LLMs for code-switched languages.

Can You Translate for Me? Code-Switched Machine Translation with Large Language Models

Resources for building NLP applications, such as data and models, are usually only created and curated for a limited set of high resource languages. Thus, the ability to transfer knowledge to a new language is a key way in which to enable access to NLP technology for a wider population. This paper presents a framework to perform zero-shot inference in a target language by using cross-lingual retrieval from another language where limited annotated data for a comparable domain is available. Results on two large-scale multilingual datasets show that, in this setup, this framework improves over fine-tuning multilingual models or translating annotated data, and achieves results relatively close to fine-tuning the model on the target language directly. These results show that models can be transferred efficiently across languages for a given task and domain, even for languages not covered by multilingual model training approaches.

Efficient Zero-Shot Cross-lingual Inference via Retrieval

Both standalone language models (LMs) as well as LMs within downstream-task systems have been shown to generate statements which are factually untrue. This problem is especially severe for low-resource languages, where training data is scarce and of worse quality than for high-resource languages. In this opinion piece, we argue that LMs in their current state will never be fully trustworthy in critical settings and suggest a possible novel strategy to handle this issue: by building LMs such that can cite their sources -- i.e., point a user to the parts of their training data that back up their outputs. We first discuss which current NLP tasks would or would not benefit from such models. We then highlight the expected benefits such models would bring, e.g., quick verifiability of statements. We end by outlining the individual tasks that would need to be solved on the way to developing LMs with the ability to cite. We hope to start a discussion about the field's current approach to building LMs, especially for low-resource languages, and the role of the training data in explaining model generations.

Who Are All The Stochastic Parrots Imitating? They Should Tell Us!

Aspect-based sentiment analysis (ABSA) is a natural language processing problem that analyzes user-generated reviews to determine a) the target entity being reviewed, b) the high-level aspect to which it belongs, 
and c) the sentiment expressed toward the targets and the aspects. 
Numerous yet scattered corpora for ABSA make it difficult for researchers to identify corpora best suited for a specific ABSA subtask quickly. This study presents a database of corpora that can be used to train and evaluate autonomous ABSA systems. 
Additionally, we provide an overview of the major corpora for ABSA and its subtasks and highlight several features that researchers should consider when selecting a corpus. Finally, we discuss the advantages and disadvantages of existing dataset collection approaches and make recommendations for future corpora creation. This survey examines 98 publicly available ABSA datasets covering over 25 domains, including 77 English and 21 other languages datasets (https://github.com/RiTUAL-UH/ABSA-Datasets-Info).

A Review of Datasets for Aspect-based Sentiment Analysis

This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets, using 23 data sets covering 8 different common NLP application tasks. We extensively evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. ChatGPT suffers from hallucination problems like other LLMs.Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn "prompt engineering" fashion.We also release a codebase for evaluation set extraction.

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios. However, existing few-shot intent detection methods either ignore the intent labels, (e.g. treating intents as indices) or do not fully utilize this information (e.g. only using part of the intent labels). In this work, we present an end-to-end One-to-All system that enables the comparison of an input utterance with all label candidates. The system can then fully utilize label semantics in this way. Experiments on three few-shot intent detection tasks demonstrate that One-to-All is especially effective when the training resource is extremely scarce, achieving state-of-the-art performance in 1-, 3- and 5-shot settings. Moreover, we present a novel pretraining strategy for our model that utilizes indirect supervision from paraphrasing, enabling zero-shot cross-domain generalization on intent detection tasks. Our code is at https://github.com/jiangshdd/AllLablesTogether.

All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm

News is read and consumed differently based on its topic and timeliness to the reader. Some stories attract readers immediately after they are published, while others capture readership consistently over multiple days after their publication, regardless of their overall popularity. This paper studies this less explored facet of news story consumption, which we name persistence, operationalized as the time for a story to reach a certain percent of its total interest. In particular, we study persistence though a novel, publicly available data set of news tweets from 353 news outlets. We perform an extensive linguistic analysis of news persistence in social media to uncover the underlying topical and stylistic cues that impact short- or long-term interest in a story. We train several models for predicting news persistence that achieve predictive performance of up to 0.353 Spearman correlation when extrapolating to tweets from days unseen in training and retain significant predictive performance even on tweets from accounts unseen in training. The ability to predict news persistence can be useful in several practical applications that drive news and social media consumption including alerting, search ranking or recommendations.

Analyzing and Predicting Persistence of News Tweets

When deploying a machine learning model, one should aim not only to optimize performance metrics such as accuracy but also care about model fairness and reliability. Fairness means that the model is prevented from learning spurious correlations between a target variable and socio-economic attributes, and is generally achieved by applying debiasing techniques. Model reliability stems from the ability to determine whether we can trust model predictions for the given data. This can be achieved using uncertainty estimation (UE) methods. Debiasing and UE techniques potentially interfere with each other, raising the question of whether we can achieve both reliability and fairness at the same time. This work aims to answer this question empirically based on an extensive series of experiments combining state-of-the-art UE and debiasing methods, and examining the impact on model performance, fairness, and reliability.

Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?

Deriving value from a conversational AI system depends on the capacity of a user to translate the prior knowledge into a configuration. In most cases, discovering the set of relevant turn-level speaker intents is often one of the key steps. Purely unsupervised algorithms provide a natural way to tackle discovery problems but make it difficult to incorporate constraints and only offer very limited control over the outcomes. Previous work has shown that semi-supervised (deep) clustering techniques can allow the system to incorporate prior knowledge and constraints in the intent discovery process. But they did not address how to allow for control through human feedback. In our Controllable Discovery of Intents (CDI) framework domain and prior knowledge are incorporated using a sequence of unsupervised contrastive learning on unlabeled data followed by fine-tuning on partially labeled data, and finally iterative refinement of clustering and representations through repeated clustering and pseudo-label fine-tuning. In addition, we draw from continual learning literature and use learning-without-forgetting to prevent catastrophic forgetting across those training stages. Finally, we show how this deep-clustering process can become part of an incremental discovery strategy with human-in-the-loop. We report results on both CLINC and BANKING datasets. CDI outperforms previous works by a significant margin: 10.26% and 11.72% respectively.

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