Canada

Despite the rapid development of neural-based models, syntax still plays a crucial role in modern natural language processing. However, few studies have incorporated syntactic information into ancient Chinese understanding tasks due to the lack of syntactic annotation. This paper explores the role of syntax in ancient Chinese understanding based on the noisy syntax trees from unsupervised derivation and modern Chinese syntax parsers. On top of that, we propose a novel syntax encoding component -- confidence-based syntax encoding network (cSEN) to alleviate the side effects from the existing noise caused by unsupervised syntax derivation and the incompatibility between ancient and modern Chinese. Experiments on two typical ancient Chinese understanding tasks, ancient poetry theme classification and ancient-modern Chinese translation, demonstrate that syntactic information can effectively enhance the understanding of ancient Chinese over strong baselines, and that the proposed cSEN plays an important role in noisy scenarios.

ACL 2023

Enhancing Ancient Chinese Understanding with Derived Noisy Syntax Trees

unsupervised syntax parsing

syntax encoding

ancient chinese understanding

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
<br>The conference will be held in Toronto, Canada, July 9-14, 2023.<br><br>
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.<br>
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.

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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.

Although Shapley values have been shown to be highly effective for identifying harmful training instances, dataset size and model complexity constraints limit the ability to apply Shapley-based data valuation to fine-tuning large pre-trained language models. To address this, we propose TS-DShapley, an algorithm that reduces computational cost of Shapley-based data valuation through: 1) an efficient sampling-based method that aggregates Shapley values computed from subsets for valuation of the entire training set, and 2) a value transfer method that leverages value information extracted from a simple classifier trained using representations from the target language model. Our experiments applying TS-DShapley to select data for fine-tuning BERT-based language models on benchmark natural language understanding (NLU) datasets show that TS-DShapley outperforms existing data selection methods. Further, TS-DShapley can filter fine-tuning data to increase language model performance compared to training with the full fine-tuning dataset.

Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values

Transformer-based language models have been recently showcasing impressive performance on a number of common-sense reasoning tasks such as the Winograd Schema Challenge (WSC) while continuing to struggle on task instances that are either slightly re-worded or perturbed. In the following paper, we wish to address these issues by re-framing the WSC using a never-ending learning, human-in-the-loop scenario devised specifically for perturbed pronoun co-reference resolution problems. We introduce EvoGrad, an open-source, user-friendly platform for the continual evaluation and development of models, based on iterations of human-adversarial perturbations. Given that common-sense knowledge varies cross-culturally and through time, our platform allows for the communal contribution towards an evolving task that is both inclusive and accessible to wider societies. In addition, we propose a novel mechanism to develop new task instances, and define a new metric to measure model stability on such dynamic tasks, called the Minimum Error Depth. We show that models fine-tuned on a small iteration of EvoGrad have their performance boosted on WSC-based tasks; this indicates a promising synergy between the acquisition of common sense and the never-ending learning paradigm.

EvoGrad: An Online Platform for an Evolving Winograd Schema Challenge using Adversarial Human Perturbations

Scientific research is inherently shaped by its authors' perspectives, influenced by various factors
such as their personality, community, or society. Junior researchers often face challenges in identifying the perspectives reflected in the existing literature and struggle to develop their own viewpoints. In response to this issue, we introduce PersLEARN , a tool designed to facilitate the cultivation of scientific perspectives, starting from a basic seed idea and progressing to a well-articulated framework. By interacting with a prompt-based model, researchers can develop their perspectives explicitly. Our human
study reveals that scientific perspectives developed by students using PersLEARN exhibit a superior level of logical coherence and depth compared to those that did not. Furthermore, our pipeline outperforms baseline approaches across multiple domains of literature from various perspectives. These results suggest that PersLEARN could help foster a greater appreciation of diversity in scientific perspectives as an essential component of research training.

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PersLEARN: Research Training through the Lens of Perspective Cultivation

Pre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks. However, concerns persist regarding the presence of hidden biases within these models, which can lead to discriminatory outcomes and reinforce harmful stereotypes. To address this issue, we propose Finspector, a human-centered visual inspection tool designed to detect biases in different categories through log-likelihood scores generated by language models. The goal of the tool is to enable researchers to easily identify potential biases using visual analytics, ultimately contributing to a fairer and more just deployment of these models in both academic and industrial settings. Finspector is available at https://github.com/IBM/finspector.

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Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models

We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala (a school of Sanskrit) to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification. Our systems currently report state-of-the-art performance on available benchmark datasets for all tasks. SanskritShala is deployed as a web-based application, which allows a user to get real-time analysis for the given input. It is built with easy-to-use interactive data annotation features that allow annotators to correct the system predictions when it makes mistakes. We publicly release the source codes of the 4 modules included in the toolkit, 7 word embedding models that have been trained on publicly available Sanskrit corpora and multiple annotated datasets such as word similarity, relatedness, categorization, analogy prediction to assess intrinsic properties of word embeddings. So far as we know, this is the first neural-based Sanskrit NLP toolkit that has a web-based interface and a number of NLP modules. We are sure that the people who are willing to work with Sanskrit will find it useful for pedagogical and annotative purposes. SanskritShala is available at: https://cnerg.iitkgp.ac.in/sanskritshala. The demo video of our platform can be accessed at: https://youtu.be/x0X31Y9k0mw4.

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SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes

English is acknowledged worldwide as a mode of communication. However, due to the absence of realistic practicing scenarios, students learning English as a foreign language (EFL) typically have limited chances to converse and share feedback with others. In this paper, we propose PEEP-Talk, a real-world situational dialogue-based chatbot designed for English education. It also naturally switches to a new topic or situation in response to out-of-topic utterances, which are common among English beginners. Furthermore, PEEP-Talk provides feedback score on conversation and grammar error correction. We performed automatic and user evaluations to validate performance and education efficiency of our system. The results show that PEEP-Talk generates appropriate responses in various real-life situations while providing accurate feedback to learners. Moreover, we demonstrate a positive impact on English-speaking, grammar, and English learning anxiety, implying that PEEP-Talk can lower the barrier to learning natural conversation in effective ways.

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PEEP-Talk: A Situational Dialogue-based Chatbot for English Education

Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models. Alfred provides a simple Python interface for the key steps of this emerging paradigm, with a high-throughput backend for large-scale data labeling. Users can quickly create, evaluate, and refine their prompt-based weak supervision sources; map the results to weak labels; and resolve their disagreements with a label model. Alfred enables a seamless local development experience backed by models served from self-managed computing clusters. It automatically optimizes the execution of prompts with optimized batching mechanisms. We find that this optimization improves query throughput by 2.9x versus a naive approach. We present two example use cases demonstrating Alfred on YouTube comment spam detection and pet breeds classification. Alfred is open source, available at https://github.com/BatsResearch/alfred.

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Alfred: A System for Prompted Weak Supervision

In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communities often face the challenge of aggregating results over incomparable metrics and scenarios, which makes conclusions and take-away messages less reliable. With Ranger, we aim to address this issue by providing a task-agnostic toolkit that combines the effect of a treatment on multiple tasks into one statistical evaluation, allowing for comparison of metrics and computation of an overall summary effect. Our toolkit produces publication-ready forest plots that enable clear communication of evaluation results over multiple tasks. Our goal with the ready-to-use Ranger toolkit is to promote robust, effect-size-based evaluation and improve evaluation standards in the community. We provide two case studies for common IR and NLP settings to highlight Ranger's benefits.

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Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation

Existing multiparty dialogue datasets for coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts.  Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.

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Multilingual Coreference Resolution in Multiparty Dialogue

Recently proposed BERT-based evaluation metrics perform well on standard benchmarks but are vulnerable to adversarial attacks, e.g., relating to information correctness. We argue that this stems (in part) from the fact that they are models of semantic similarity. In contrast, we develop evaluation metrics based on \emph{Natural Language Inference} (NLI), which we deem a more appropriate modeling. We design a preference-based adversarial attack framework and show that our NLI based metrics are much more robust to the attacks than the recent BERT-based metrics. On standard benchmarks, our NLI based metrics outperform existing summarization metrics, but perform below SOTA MT metrics. However, when combining existing metrics with our NLI metrics, we obtain both higher adversarial robustness (15\%-30\%) and higher quality metrics as measured on standard benchmarks (+5\% to 30\%).

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