Taiwan

Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users&#39; perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our framework was evaluated more favorably in all dimensions including competence and friendliness, compared to the end-to-end model which does not explicitly handle social content or factual questions

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.31/)

IJCNLP-AACL 2022

Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue

social sciences

conditional generation

persuasion

dialogue systems

### 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

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our framework was evaluated more favorably in all dimensions including competence and friendliness, compared to the end-to-end model which does not explicitly handle social content or factual questions

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.31/)

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). 

Communicating efficiently in natural language requires that we often leave information implicit, especially in spontaneous speech. This frequently results in phenomena of incompleteness, such as omitted references, that pose challenges for language processing. In this poster session, we discuss the state of the art in research regarding the automatic processing of such implicit references in dialog scenarios, discuss weaknesses with respect to inconsistencies in task definitions and terminologies, and outline ideas for future work. Among others, these include unification of existing tasks and evaluation metrics, addressing data scarcity, and taking into account model and annotator uncertainties, but we are also excited to exchange new ideas about where this field should head.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.45/)

Towards Implicit References in Dialog

Speakers repeat constructions frequently in dialogue. Due to their peculiar information-theoretic properties, repetitions can be thought of as a strategy for cost-effective communication. In this study, we focus on the repetition of lexicalised constructions -- i.e., recurring multi-word units -- in English open-domain spoken dialogues. We hypothesise that speakers use construction repetition to mitigate information rate, leading to an overall decrease in utterance information content over the course of a dialogue. We conduct a quantitative analysis, measuring the information content of constructions and that of their containing utterances, estimating information content with an adaptive neural language model. We observe that construction usage lowers the information content of utterances. This facilitating effect (i) increases throughout dialogues, (ii) is boosted by repetition, (iii) grows as a function of repetition frequency and density, and (iv) is stronger for repetitions of referential constructions.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.51/)

Construction Repetition Reduces Information Rate in Dialogue

Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale. In this paper we investigate the challenging task of less-data constrained generation, especially when the generated news headlines are short yet expected by readers to keep readable and informative simultaneously. We highlight the key information modeling task and propose a novel duality fine-tuning method by formally defining the probabilistic duality constraints between key information prediction and headline generation tasks. The proposed method can capture more information from limited data, build connections between separate tasks, and is suitable for less-data constrained generation tasks. Furthermore, the method can leverage various pre-trained generative regimes, e.g., autoregressive and encoder-decoder models. We conduct extensive experiments to demonstrate that our method is effective and efficient to achieve improved performance in terms of language modeling metric and informativeness correctness metric on two public datasets.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.5/)

Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning

This paper focuses on the generation of natural language questions based on SPARQL queries, with an emphasis on conversational use cases (follow-up question-answering). It studies what can be achieved so far based on current deep learning models (namely pretrained T5 and BART models). To do so, 4 knowledge-based QA corpora have been homogenized for the task and a new challenge set is introduced. A first series of experiments analyzes the impact of different training setups, while a second series seeks to understand what is still difficult for these models. The results from automatic metrics and human evaluation show that simple questions and frequent templates of SPARQL queries are usually well processed whereas complex questions and conversational dimensions (coreferences and ellipses) are still difficult to handle. The experimental material is publicly available on https://github.com/Orange-OpenSource/sparql-to-text .

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.11/)

SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications

Knowledge graph embedding (KGE) has been well-studied in general domains, but has not been examined for food computing. To fill this gap, we perform knowledge representation learning over a food knowledge graph (KG). We employ a pre-trained language model to encode entities and relations, thus emphasizing- ing contextual information in food KGs. The model is trained on two tasks – predicting a masked entity from a given triple from the KG and predicting the plausibility of a triple. Analysis of food substitutions helps in dietary choices for enabling healthier eating behaviors. Previous work in food substitutions mainly focuses on semantic similarity while ignoring the context. It is also hard to evaluate the substitutions due to the lack of an adequate validation set, and further, the evaluation is sub- jective based on perceived purpose. To tackle this problem, we propose a collection of adversarial sample generation strategies for different food substitutions over our learnt KGE. We pro- pose multiple strategies to generate high-quality context-aware recipe and ingredient substitutions and also provide generalized ingredient substitutions to meet different user needs. The effectiveness and efficiency of the proposed knowledge graph learning method and the following attack strategies are verified by extensive evaluations on a large-scale food KG.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.50/)

Food Knowledge Representation Learning with Adversarial Substitution

In recent years, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP). Despite the increasing amount of research work in this area, a comprehensive study that categorizes established topics and reviews emerging research streams remains absent to this day. To provide an overview of the research landscape of KGs in NLP, we analyzed 507 papers in a multifaceted survey of tasks, research types, and contributions.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.46/)

A Decade of Knowledge Graphs in Natural Language Processing: A Survey

Keywords or keyphrases are often used to highlight a document's domains or main topics. Unsupervised keyphrase extraction (UKE) has always been highly anticipated because no labeled data is needed to train a model. This paper proposes an augmented graph-based unsupervised model to identify keyphrases from a document by integrating graph and deep learning methods. The proposed model utilizes mutual attention extracted from the pre-trained BERT model to build the candidate graph and augments the graph with global and local context nodes to improve the performance. The proposed model is evaluated on four publicly available datasets against thirteen UKE baselines. The results show that the proposed model is an effective and robust UKE model for long and short documents. Our source code is available on GitHub.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.19/)

AGRank: Augmented Graph-based Unsupervised Keyphrase Extraction

Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model’s lack of support for such reasoning. We present PInKS, Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, both empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.26/)

PInKS: Preconditioned Commonsense Inference with Minimal Supervision

In this research, we propose CNN based CoCNN+ model can beat transformer models in Bengali and Hindi language modeling - two of the most popular languages in the world. These languages have high inflection, flexible word order, and a lot of compound characters making them morphologically rich. Base CoCNN model has two components – 1D CNN based vocabulary learner works at word level, while terminal coordinator works at document level. We analyze CNN, LSTM and Transformer based models for Bengali language modeling using perplexity score. CNNs do a better job than LSTMs because of the flexible word order in Bengali. CoCNN has on par performance with pretrained BERT. We extend the base CoCNN to CoCNN+ by incorporating residual connection in the shallow layers of vocabulary learner and by introducing parallel branches of different convolution widths in the terminal coordinator. These modifications help CoCNN beat pretrained BERT for Bengali and Hindi next word prediction, text classification and sentiment analysis tasks.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.4/)

CNN for Modeling Sanskrit Originated Bengali and Hindi Language

Word embeddings and pre-trained language models have become essential technical elements in natural language processing. While the general practice is to use or fine-tune publicly available models, there are significant advantages in creating or pre-training unique models that match the domain. The performance of the models degrades as language changes or evolves continuously (semantic shift), but the high cost of model building inhibits regular re-training, especially for the language models. This study designs a methodology for observing time-series performance degradation of word embeddings and pre-trained language models using semantic shift in a corpus. We define an efficiently computable metric named Semantic Shift Stability based on the degree of semantic shift. In the experiments, we create models that vary by time series and reveal the performance degradation in two datasets, Japanese and English. Several case studies demonstrate that Semantic Shift Stability supports decision-making as to whether a model should be re-trained. The source code is available at https://github.com/Nikkei/semantic-shift-stability.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.17/)

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