Indonesia

Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer in conversations such as task-oriented dialogues, existing approaches suffer from these limitations as context can play an important role and the style attributes are often difficult to define in conversations. In this paper, we introduce conversation style transfer as a few-shot learning problem, where the model learns to perform style transfer by observing only a few example dialogues in the target style. We propose a novel in-context learning approach to solve the task with style-free dialogues as a pivot. Human evaluation shows that by incorporating multi-turn context, the model is able to match the target style while having better appropriateness and semantic correctness compared to utterance/sentence-level style transfer. Additionally, we show that conversation style transfer can also benefit downstream tasks. For example, in multi-domain intent classification tasks, the F1 scores improve after transferring the style of training data to match the style of the test data.

IJCNLP-AACL 2023

Conversation Style Transfer using Few-Shot Learning

### 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
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
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.

poster

### 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

Previous studies exploring the human-like capabilities of machine-learning models have primarily focused on pure language models. 
Limited attention has been given to investigating whether models exhibit human-like behavior when performing tasks that require the integration of visual and language information.
In this study, we investigate the impact of tags of semantic, phonological, and bilingual features on the visual question answering task performance of an unsupervised model. 
Our findings reveal its similarities with the influence of distractors in the picture-naming task (known as the picture-word-interference paradigm) observed in human experiments: 

1) Semantically-related tags have a more negative effect on task performance compared to unrelated tags, indicating a more robust competition between visual and tag information which are semantically closer to each other when generating an answer. 
2) Even presenting a partial section (wordpiece) of the originally detected tag significantly improves task performance, with the portion that plays a lesser role in determining the overall meaning of the original tag leading to a more pronounced improvement. 
3) Tags in two languages that refer to the same meaning exhibit a symmetrical-like effect on performance in balanced bilingual models.

Datasets and code of this project are released at https://github.com/NLPbelllabs/PWI.

Human-Like Distractor Response in Vision-Language Model

Most work on verbalising Knowledge-Graphs (KG) has focused on high-resource languages such as English, Russian, Czech or Arabic. In this paper, we focus on KG-to-Text generation where the output text is in Breton, Irish or Welsh. To overcome the small size of the parallel training data, we combine the strengths of a multilingual language model with denoising fine-tuning on monolingual data and Soft Prompt fine-tuning on a small quantity of KG/text data. We furthermore structure the soft prompt into multiple sub-prompts designed to capture the similarities and differences between English, Knowledge graphs and the three target languages. Our experiments show that our approach outperforms strong baselines and that all sub-prompts contribute to performance.

Phylogeny-Inspired Soft Prompts For Data-to-Text Generation in Low-Resource Languages

Prior work has shown that the ordering in which concepts are shown to a commonsense generator plays an important role, affecting the quality of the generated sentence. However, it remains a challenge to determine the optimal ordering of a given set of concepts such that a natural sentence covering all the concepts could be generated from a pretrained generator. To understand the relationship between the ordering of the input concepts and the quality of the generated sentences, we conduct a systematic study considering multiple language models (LMs) and concept ordering strategies. We find that BART-large model consistently outperforms all other LMs considered in this study when fine-tuned using the ordering of concepts as they appear in CommonGen training data as measured using multiple evaluation metrics. Moreover, the larger GPT3-based large language models (LLMs) variants do not necessarily outperform much smaller LMs on this task, even when fine-tuned on task-specific training data. Interestingly, human annotators significantly reorder input concept sets when manually writing sentences covering those concepts, and this ordering provides the best sentence generations independently of the LM used for the generation, outperforming a probabilistic concept ordering baseline.

Learning to Predict Concept Ordering for Common Sense Generation

The escalating volume and frequency of social media complaints necessitate robust automated complaint analysis techniques. Much of the existing body of research in this area has been devoted to two primary aspects: identifying complaint-specific content amidst other non-complaint communications, and predicting the severity of a complaint, which involves classifying complaints into different severity levels based on the anticipated resolution from the complainant's perspective. These automated analysis tools equip companies with the means to effectively manage complaints and generate suitable responses. In our study, we present a unified generative approach for complaint detection, transforming the multitask learning problem into a text-to-text generation task. As part of our training strategy, we adopt the Seq2Path training paradigm that conceptualizes the outcome as a tree structure as opposed to a traditional sequence. This innovative approach tackles the drawbacks of conventional sequences, such as the lack of order among the outputs, yielding a more coherent and structured output. Our model's effectiveness is assessed against the benchmark \textit{Complaints} dataset, highlighting its superior performance across diverse evaluation metrics when compared with state-of-the-art models and other baselines.

Reimagining Complaint Analysis: Adopting Seq2Path for a Generative Text-to-Text Framework

Having heard "a pimwit", English-speakers assume that "the pimwit" is also possible. This type of productivity is attributed to syntactic categories such as NOUN and DETERMINER, but the key question is how do humans become endowed with these categories in the first place. We propose a novel approach that combines corpus analysis with computational modeling to analyze the productivity of DETERMINER+NOUN constructions in child-produced utterances. Our experiments on two corpora of child-adult interactions using two different methods of quantifying linguistic productivity show that children do not display productivity at early stages. Using a model trained on child-directed utterances, we simulate children's developmental trajectory with great precision, suggesting that the emergence of productivity in human language can be explained without the need to postulate a priori access to syntactic categories.

Linguistic Productivity: the Case of Determiners in English

Volatility, a crucial statistical measure in the financial market, serves as an indicator of financial instrument risk. Accurate volatility capture aids in predicting stock movements and is valuable in derivative trading, such as options trading. While recent research focuses on volatility forecasting using earnings call transcriptions, most approaches rely on end-to-end models that directly process textual or vocal data. However, limited efforts have been made to simulate the reading and comprehension processes of financial professionals, thereby enhancing the capabilities of language models. To address this gap, we propose a general numeral attachment dataset designed to train language models to understand earnings calls with the expertise of professionals. Additionally, we introduce a pre-training process that improves the semantic understanding of earnings calls. Experimental results demonstrate that our pre-trained language model enhances the accuracy of 3-day volatility forecasting.

Enhancing Volatility Forecasting in Financial Markets: A General Numeral Attachment Dataset for Understanding Earnings Calls

Critical error detection (CED) aims to identify the presence of catastrophic meaning distortion in machine translation. Fatal errors require significant attention because of their potential to cause personal or societal harm. The CED for Korean, an agglutinative language, is particularly highlighted, as minor variations in morphemes often bring substantial shifts in semantic interpretation. However, research on Korean is still underexplored and has room for improvement. In this study, we conduct the first investigation of CED for English-Korean to the best of our knowledge. We adopt prompt-based fine-tuning and propose various informative evidence to incorporate into the input prompt. Subsequently, we perform comprehensive verification and analysis to identify the most helpful guidance for detecting critical errors. The experimental results show that prompt-based fine-tuning with informative evidence outperforms standard fine-tuning by a large margin, demonstrating its remarkable effectiveness in English-Korean CED.

Informative Evidence-guided Prompt-based Fine-tuning for English-Korean Critical Error Detection

Reinforcement learning (RL) offers a principled framework for optimizing a given reward function and has been applied to a neural machine translation (NMT) problem to maximize arbitrary task metrics. However, previously adopted RL algorithms for NMT (e.g., policy gradient) are generally slow as they require online data collection, and limits the algorithm's applicability to specific reward functions that can be evaluated online.
In this paper, we present an offline RL algorithm called CER (conservative expectile regression). Despite the demanding nature of offline RL tasks, which are even more difficult with large models, this algorithm is capable to learn stably by explicitly exploiting the properties of NMT's RL formulation, such as the deterministic transition function. We analyze and discuss the design choices of CER, and demonstrate in the experiments that the proposed method outperforms its competitors for offline reward optimization in NMT.

Improving Neural Machine Translation with Offline Evaluations

Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language processing. This paper explores whether fully incremental dependency parsing with modern architectures can be competitive. We build parsers combining strictly left-to-right neural encoders with fully incremental sequence-labeling and transition-based decoders. The results show that fully incremental parsing with modern architectures considerably lags behind bidirectional parsing, noting the challenges of psycholinguistically plausible parsing.

On the Challenges of Fully Incremental Neural Dependency Parsing

Counterspeech can be an effective method for battling hateful content on social media. Automated counterspeech generation can aid in this process. Generated counterspeech, however, can be viable only when grounded in the context of topic, audience and sensitivity as these factors influence both the efficacy and appropriateness. In this work, we propose a novel framework based on theories of discourse to model inferential links of the context. Within this framework, we propose: i) a taxonomy of counterspeech derived from discourse frameworks, and ii) discourse-informed prompting strategies for generating contextually-grounded counterspeech. To construct and validate this framework, we present a process for collecting an in-the-wild dataset of counterspeech from Reddit. Using this process, we manually annotate a dataset of 3.9k Reddit comment pairs for the presence of hatespeech and counterspeech. The positive pairs are annotated for 10 classes in our proposed taxonomy. We annotate these pairs with style-transferred counterparts to remove offensiveness and first-person references. We show that by using our dataset and framework, large language models can generate contextually-grounded counterspeech informed by theories of discourse. According to our human evaluation, our approaches can act as a safeguard against critical failures of discourse-agnostic models.

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