Indonesia

Code-mixing is a common linguistic phenomenon when words or syllables from two or more languages are used within a single sentence. Several works have been conducted on building such datasets and performing downstream tasks on them. Although it is very common to observe code-mixing with multiple languages, most datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce OffMix-3L, a novel dataset for the task of offensive language identification which contains code-mixed data from 3 different languages. We also provide a comprehensive experimental analysis with several models and observe that BanglishBERT outperforms all the transformer-based models and also the generative model GPT-3.5 on OffMix-3L.

IJCNLP-AACL 2023

OffMix-3L: A Novel Code-Mixed Test Dataset in Bangla-English-Hindi for Offensive Language Identification

### 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

workshop paper

Controllable text generation is a fundamental aspect of natural language generation, with numerous methods proposed for different constraint types. However, these approaches often require significant architectural or decoding modifications, making them challenging to apply to additional constraints or resolve different constraint combinations. To address this, our paper introduces Regular Expression Instruction (REI), which utilizes an instruction-based mechanism to fully exploit regular expressions' advantages to uniformly model diverse constraints. Specifically, our REI supports all popular fine-grained controllable generation constraints, i.e., lexical, positional, and length, as well as their complex combinations, via regular expression-style instructions. Our method only requires fine-tuning on medium-scale language models or few-shot, in-context learning on large language models, and requires no further adjustment when applied to various constraint combinations. Experiments demonstrate that our straightforward approach yields high success rates and adaptability to various constraints while maintaining competitiveness in automatic metrics and outperforming most previous baselines.

Toward Unified Controllable Text Generation via Regular Expression Instruction

Solving math word problems (MWP) is a challenging task for natural language processing systems, as it requires to not only identify and comprehend the problem description within the context, but also to deduce a solution in accordance with the posed question. Previous solvers have been found to prioritize the context over the question, resulting in low performance when solving multiple questions under the same context. In this paper, we present a question-oriented strategy to address this issue and improve the generalizability of MWP solvers. Our approach features an entity-aware encoder that enhances the connection between MWP context and question via entities in established dependency graphs, aiming at obtaining better problem representations. Then, a question-guided decoder is trained using a contrastive learning strategy to enhance the question representations. Empirical evaluations on four benchmarks demonstrate that our method outperforms previous solvers and exhibits a favorable balance between efficacy and efficiency in MWP solving. In addition, our solver is not reliant on any specific pre-trained model and demonstrates seamless compatibility with different pre-trained model backbones.

Don't be Blind to Questions: Question-Oriented Math Word Problem Solving

Detecting out-of-scope (OOS) utterances is crucial in task-oriented dialogue systems, but obtaining enough annotated OOS dialogues to train a binary classifier directly is difficult in practice. Existing data augmentation methods generate OOS dialogues automatically, but their performance usually depends on an external corpus. This dependence not only induces uncertainty, but also reduces the quality of generated dialogues. Specifically, all of them are out-of-domain (OOD). 

Herein we propose SILVER, a self data augmentation method that does not use external data. It addresses issues of previous research and improves the accuracy of OOS detection (false positive rate: 90.5% to 47.4%). Furthermore, SILVER successfully generates high-quality in-domain (IND) OOS dialogues in terms of naturalness (percentage: 8% to 68%) and OOS correctness (percentage: 74% to 88%), as evaluated by human workers.

SILVER: Self Data Augmentation for Out-of-Scope Detection in Dialogues

State-of-the-art summarization systems can generate highly fluent summaries. These summaries, however, may contain factual inconsistencies and/or information not present in the source. Hence, an important component of assessing the quality of summaries is to determine whether there is information consistency between the source and the summary. Existing approaches are typically based on lexical matching or representation-based methods. In this work, we introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared. We propose a Multiple-choice Question Answering and Generation framework, MQAG, which approximates the information consistency by computing the expected statistical distance between summary and source answer distributions over automatically generated multiple-choice questions. This approach exploits multiple-choice answer probabilities, as predicted answer distributions can be compared. We conduct experiments on four summary evaluation datasets: QAG-CNNDM/XSum, XSum-Hallucination, Podcast Assessment, and SummEval. Experiments show that MQAG, using models trained on SQuAD or RACE, outperforms existing evaluation methods on the majority of tasks.

MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization

Generating high-quality non-English language datasets is crucial for achieving high performance in various Natural Language Processing (NLP) tasks. In this paper, we propose a new approach for translating NLP datasets that relies on a two-phase pipeline and online translation services. Our approach focuses on solving the alignment problem that affects span prediction tasks and utilizes automatically labeled data for training an alignment model. We demonstrate that our model-based approach shows higher accuracy than any other alignment method and improves the average F1 score on several Question-Answering (QA) datasets, specifically on the XQuAD Translated-train dataset, achieving new state-of-the-art results.

Automatic Translation of Span-Prediction Datasets

While both text and Knowledge Graphs (KG) may be used to answer a question, most current Question Answering and Generation models only work on a single modality. In this paper, we introduce a multi-task model such that questions can be generated and answered from both KG and text. The model has wide coverage and handles both simple (one KG fact) and complex (more than one KG fact) questions. Extensive internal, cross-modal and external consistency checks, and analysis of the quality of the generated questions, show that our approach outperforms previous work. Our data and modeling also leads to improvements in downstream tasks, including better performance with finetuning Open-Domain QA architectures and better correlation with human judgments than the Data-QuestEval metric which was previously proposed for evaluating the semantic adequacy of KG-to-Text generations.

Generating and Answering Simple and Complex Questions from Text and from Knowledge Graphs

Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM) have shown that transformer LM encoder based similarity metrics transfer well for QA evaluation, but they are limited by the usage of a single correct reference answer. We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation), using multiple reference answers (combining multiple correct and incorrect references) for sentence-form QA. We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems, across multiple academic and industrial datasets, and show that it outperforms previous baselines and obtains the highest correlation with human annotations.

SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References

With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models, and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets on hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion.

Attacking Open-domain Question Answering by Injecting Misinformation

The strong capabilities of conversation-based large language models in healthcare applications are attracting an increasingly larger audience. However, the reliability of these models in consistently and accurately providing medical advice based on user-inputted symptoms is a critical concern. This study explores the sensitivity of LLMs to variations in user input, focusing specifically on how different symptom descriptions and prior users' beliefs can potentially lead to different diagnoses. We test two GPT models with five different prompt templates to assess their ability of mentioning the true patient condition based on the symptoms. Our findings reveal a substantial sensitivity to input variations---especially when users have prior assumptions and beliefs---indicating potential inconsistencies in the diagnoses generated by these models.

“Dr LLM, what do I have?”: The Impact of User Beliefs and Prompt Formulation on Health Diagnoses

In clinical visits, clinical note writing is a time-consuming and cost-prohibitive manual task for clinicians. Although virtual medical scribes have been proposed to generate clinical notes (semi-)automatically, the data sparsity issue is still a challenging problem in practice. Identifying the topic of clinical utterances in doctor-patient conversations is one of the key strategies for automation. In this paper, we propose an utterance-level note section classification method for the situation of the limited amount of in-house data. We leverage an external, unsupervised corpus of medical conversations to transfer knowledge using the framework of Unsupervised Meta-learning with Task Augmentation (UMTA). Our experiments are performed on both manual transcripts and machine transcripts generated by automatic speech recognition (ASR). The results show that our strategies achieve substantial gains in prediction accuracy over several baseline approaches and are robust to ASR errors.

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