Indonesia

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&#39; 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.

IJCNLP-AACL 2023

Toward Unified Controllable Text Generation via Regular Expression Instruction

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
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so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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.

technical paper

### 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

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

Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by a lack of singleton mention span data as well as other entity information. This paper presents a coreference model that learns singletons as well as features such as entity type and information status, via a multi-task learning-based approach. This approach achieves new state-of-the-art scores on the OntoGUM benchmark (+2.7 points) and increases robustness on multiple out-of-domain datasets (+2.3 points on average), likely due to greater generalizability for mention detection and utilization of more data from singletons, when compared to only coreferent mention pair matching.

Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization

Over the past several years, demand and popularity of using virtual assistants to finish jobs like service scheduling and online shopping have increased. While keeping the user's request in mind, an effective task-oriented virtual agent must strive to improve the seller's profit. Therefore, in order to achieve the best possible trade-off between the parties, this form of virtual agents has to have strong negotiating abilities. Although current conversational agents are quite good at making fluent sentences, they are still unable to use strategic thinking. In order to more effectively contextualize the choice of the next set of negotiation methods while producing answers, we develop Nego-GAT, an end-to-end negotiation system that includes sentiment information and graph attention embedding into GPT-2. Our self-supervised model beats earlier cutting-edge negotiation models in terms of both the precision of strategy/dialogue act prediction and the caliber of the generated dialogue responses.

Sentiment Aided Graph Attentive Contextualization for Task Oriented Negotiation Dialogue Generation

Most benchmarks designed for question answering over knowledge bases (KBQA) operate with the i.i.d. assumption where one encounters the same schema items during inference as those observed during training. Recently, the GrailQA dataset was established to evaluate zero-shot generalization capabilities of KBQA models as a departure from the i.i.d. assumption. Reasonable performance of current KBQA systems on the zero-shot GrailQA split hints that the field might be moving towards more generalizable systems. In this work, we observe a bias in the GrailQA dataset towards simpler one or two-hop questions, which results in an inaccurate assessment of the aforementioned prowess. We propose GrailQA++, a challenging zero-shot KBQA test set that contains more questions relying on complex reasoning. We leverage the concept of graph isomorphisms to control the complexity of the questions and to ensure that our proposed test set has a fair distribution of simple and complex questions. Existing KBQA models suffer a substantial drop in performance on our constructed new test set as compared to the GrailQA zero-shot split. Our analysis reveals how isomorphisms can be used to understand the complementary strengths of different KBQA models and provide a deeper insight into model mispredictions. Overall, our paper highlights the \textit{non-generalizability} of existing models and the necessity for designing more challenging benchmarks. Our dataset is available at \url{https://github.com/sopankhosla/GrailQA-PlusPlus}

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