China

Automatic Speech Recognition (ASR) is a fundamental and important task in the field of speech and natural language processing. It is an inherent building block in many applications such as voice assistant, speech translation, etc. Despite the advancement of ASR technologies in recent years, it is still inevitable for modern ASR systems to have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc. Therefore, the error correction in ASR is crucial. 


Motivated by this, this paper studies ASR error correction in the Chinese language, which is one of the most popular languages and enjoys a large number of users in the world. We first create a benchmark dataset named \emph{ASR-EC} that contains a wide spectrum of ASR errors generated by industry-grade ASR systems. To the best of our knowledge, it is the first Chinese ASR error correction benchmark. Then, inspired by the recent advances in \emph{large language models (LLMs)}, we investigate how to harness the power of LLMs to correct ASR errors. We apply LLMs to ASR error correction in three paradigms. The first paradigm is prompting, which is further categorized as zero-shot, few-shot, and multi-step. The second paradigm is finetuning, which finetunes LLMs with ASR error correction data. The third paradigm is multi-modal augmentation, which collectively utilizes the audio and ASR transcripts for error correction. Extensive experiments reveal that prompting is not effective for ASR error correction. Finetuning is effective only for a portion of LLMs. Multi-modal augmentation is the most effective method for error correction and achieves state-of-the-art performance.

EMNLP 2025

ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction

automatic speech recongnition

large language model

error correction

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems
has been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from historical customer‑agent conversations to automatically build a knowledge base. Fine‑tuning a lightweight LLM on internal data demonstrates state-of-the-art performance, outperforming larger closed-source LLMs. More specifically, empirical evaluation on 20 companies demonstrates that the proposed AI Knowledge Assist system that leverages the LLaMA-3.1-8B model eliminates the cold‑start gap in contact centers by achieving above 90\% accuracy in answering information‑seeking questions. This enables immediate deployment of RAG-powered chatbots.

AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents

Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with cold-start and data-sparse scenarios. Recent advancements in pre-trained large language models (LLMs) with rich semantic knowledge, offer promising solutions to these challenges. However, deploying LLMs at scale is hindered by their significant computational demands and latency. In this paper, we propose a novel and scalable LLM-RecSys framework, LLMInit, designed to integrate pretrained LLM embeddings into CF models through selective initialization strategies. Specifically, we identify the embedding collapse issue observed when CF models scale and match the large embedding sizes in LLMs and avoid the problem by introducing efficient sampling methods, including, random, uniform, and variance-based selections. Comprehensive experiments conducted on multiple real-world datasets demonstrate that LLMInit significantly improves recommendation performance while maintaining low computational costs, offering a practical and scalable solution for industrial applications. To facilitate industry adoption and promote future research, we provide open-source access to our implementation at https://github.com/DavidZWZ/LLMInit.

LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation

Effective product schema modeling is fundamental to e-commerce success, enabling accurate product discovery and superior customer experience. However, traditional manual schema modeling processes are severely bottlenecked, producing fewer tens of attributes per month—insufficient for modern e-commerce platforms managing thousands of product types. This paper introduces AttributeForge, the first framework to automate end-to-end product schema modeling using Large Language Models (LLMs). Our key innovation lies in orchestrating 43 specialized LLM agents through strategic workflow patterns to handle the complex interdependencies in schema generation. The framework incorporates two novel components: MC2-Eval, a comprehensive validation system that assesses schemas against technical, business, and customer experience requirements; and AutoFix, an intelligent mechanism that automatically corrects modeling defects through iterative refinement. Deployed in production, AttributeForge achieves an 88X increase in modeling throughput while delivering superior quality—a 59.83\% Good-to-Good conversion rate compared to 37.50\% for manual approaches. This significant improvement in both speed and quality enables e-commerce platforms to rapidly adapt their product schemas to evolving market needs.

AttributeForge: An Agentic LLM Framework for Automated Product Schema Modeling

In this work, we present the first embedding model specifically designed for Industry 4.0 applications, targeting the semantics of industrial asset operations. Given natural language tasks related to specific assets, our model retrieves relevant items and generalizes to queries involving similar assets, such as identifying sensors relevant to an asset’s failure mode. We systematically construct nine asset-specific datasets using an expert-validated knowledge base reflecting real operational scenarios. To ensure contextually rich embeddings, we augment queries with Large Language Models, generating concise entity descriptions that capture domain-specific nuances. Across five embedding models ranging from BERT (110M) to gte-Qwen (7B), we observe substantial in-domain gains: \textbf{HIT@1 +54.2\%, MAP@100 +50.1\%, NDCG@10 +54.7\%} on average. Ablation studies reveal that (a) LLM-based query augmentation significantly improves embedding quality; (b) contrastive objectives without in-batch negatives are more effective for tasks with many relevant items; and (c) balancing positives and negatives in batches is essential. We experimented out-of-domain tasks using Retrieval-Augmented Generation (RAG) pipeline.
\textbf{We open-source implementation and experiments.}

Generalized Embedding Models for Industry 4.0 Applications

Routing incoming queries to the most cost-effective LLM while maintaining response quality poses a fundamental challenge in optimizing performance-cost trade-offs for large-scale commercial systems.
We present IPR---a quality-constrained Intelligent Prompt Routing framework that dynamically selects optimal models based on predicted response quality and user-specified tolerance levels.
IPR introduces three key innovations: (1) a modular architecture with lightweight quality estimators trained on 1.5M prompts annotated with calibrated quality scores, enabling fine-grained quality prediction across model families; (2) a user-controlled routing mechanism with tolerance parameter τ in [0,1] that provides explicit control over quality-cost trade-offs; and (3) an extensible design using frozen encoders with model-specific adapters, reducing new model integration from days to hours. 
To rigorously train and evaluate IPR, we curate an industrial-level dataset IPRBench, a comprehensive benchmark containing 1.5 million examples with response quality annotations across 11 LLM candidates.
Deployed on a major cloud platform, IPR achieves 43.9\% cost reduction while maintaining quality parity with the strongest model in the Claude family and processes requests with sub-150ms latency.

IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs

Interpreting visual scenes with high-level reasoning is essential for many real-world applications—from autonomous systems to content moderation—but training and maintaining Vision-Language Models (VLMs) remains resource-intensive and opaque. In this work, we present CAPSTONE, a lightweight and modular framework designed for industrial settings. Instead of relying on multimodal training or fine-tuning large models, CAPSTONE transforms outputs from off-the-shelf vision models into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). This plug-and-play architecture enables reasoning over visual input without access to raw pixels, dramatically reducing computational cost and complexity. On the POPE dataset, our system—using a 7B LLM—outperforms several fully trained VLMs in zero-shot evaluations, demonstrating strong generalization without retraining. CAPSTONE offers a scalable and interpretable alternative for companies looking to integrate visual reasoning capabilities without the burden of full-scale VLM pipelines.

CAPSTONE: Composable Attribute‑Prompted Scene Translation for Zero‑Shot Vision–Language Reasoning

In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant ethical challenges, particularly in socially sensitive contexts. We introduce FairCoT, a novel framework that enhances fairness in text-to-image models through Chain-of-Thought (CoT) reasoning within multimodal generative large language models. FairCoT employs iterative CoT refinement to systematically mitigate biases, and dynamically adjusts textual prompts in real time, ensuring diverse and equitable representation in generated images. By integrating iterative reasoning processes, FairCoT addresses the limitations of zero-shot CoT in sensitive scenarios, balancing creativity with ethical responsibility. Experimental evaluations across popular text-to-image systems—including DALL-E and various Stable Diffusion variants—demonstrate that FairCoT significantly enhances fairness and diversity without sacrificing image quality or semantic fidelity. By combining robust reasoning, lightweight deployment, and extensibility to multiple models, FairCoT represents a promising step toward more socially responsible and transparent AI-driven content generation.

FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models

Large Language Models (LLMs) often exhibit social biases inherited from their training data. While existing benchmarks evaluate bias by term-based mode through direct term associations between demographic terms and bias terms, LLMs have become increasingly adept at avoiding biased responses, leading to seemingly low levels of bias. However, biases persist in subtler, contextually hidden forms that traditional benchmarks fail to capture. We introduce the Description-based Bias Benchmark (DBB), a novel dataset designed to assess bias at the semantic level that bias concepts are hidden within naturalistic, subtly framed contexts in real-world scenarios rather than superficial terms. We analyze six state-of-the-art LLMs, revealing that while models reduce bias in response at the term level, they continue to reinforce biases in nuanced settings. Data, code, and results are available at \url{https://github.com/JP-25/Description-based-Bias-Benchmark}.

What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs

We introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples, where natural language questions are translated to executable SQL queries. This representation is derived semi-automatically with minimal human intervention. We demonstrate its utility in evaluation by closely analyzing (i) the composition of widely-used benchmarks and (ii) model performance at a granular level beyond overall accuracy scores. Our analyses not only reveal example subsets that challenge all models, including those with strongest overall performance, but more importantly, specific example classes where smaller, cheaper models perform comparably to frontier models. Finally, we show a practical application of SQLSpace at inference time, using our representation to predict which natural language questions will likely yield incorrect SQL from a text-to-SQL model, and rewriting such questions to improve accuracy.

SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps

Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://anonymous.4open.science/r/Disaster_IR/.

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