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Yunyao Li

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11 papers

1 author row

AAAI Conference 2026 System Paper

PAGER: Proactive Monitoring Agent for Enterprise AI Assistant

Sujan Dutta
Junior Francisco Garcia Ayala
Pranav Pujar
Sai Sree Harsha
Dan Luo
Nikhil Vasudeva
Bikas Saha
Pritom Baruah

We present a Proactive Monitoring Agent designed for large-scale customer data platforms, such as Adobe Experience Platform (AEP), to predict and prevent workflow disruptions before they impact business operations. Unlike existing reactive solutions that assist engineers only after failures occur, our agent anticipates potential failures across multiple workflow stages, explains its predictions in natural language, and interacts with customer support engineers through a conversational interface. The system integrates a machine learning-based Prediction Module, Knowledge Graph APIs for contextual data access, and a Query Processor that powers an interactive Q&A experience, enabling timely and actionable insights to minimize operational risks and maximize business continuity.

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AAAI Conference 2025 System Paper

ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant

John Murzaku
Zifan Liu
Vaishnavi Muppala
Md Mehrab Tanjim
Xiang Chen
Yunyao Li

Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolving ambiguities in real-world, enterprise-level interactions, where context and domain-specific knowledge play a crucial role. In this demonstration, we introduce ECLAIR (Enhanced CLArification for Interactive Responses), a multi-agent framework for interactive disambiguation. ECLAIR enhances ambiguous user query clarification through an interactive process where custom agents are defined, ambiguity reasoning is conducted by the agents, clarification questions are generated, and user feedback is leveraged to refine the final response. When tested on real-world customer data, ECLAIR demonstrates significant improvements in clarification question generation compared to standard few-shot methods.

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AAAI Conference 2025 System Paper

Rewind and Render: Towards Factually Accurate Text-to-Video Generation with Distilled Knowledge Retrieval

Daniel Lee
Arjun Chandra
Yang Zhou
Yunyao Li
Simone Conia

Text-to-Video (T2V) models, despite recent advancements, struggle with factual accuracy, especially for knowledge-dense content. We introduce FACT-V (Factual Accuracy in Content Translation to Video), a system integrating multi-source knowledge retrieval into T2V pipelines. FACT-V offers two key benefits: i) improved factual accuracy of generated videos through dynamically retrieved information, and ii) increased interpretability by providing users with the augmented prompt information. A preliminary evaluation demonstrates the potential of knowledge-augmented approaches in improving the accuracy and reliability of T2V systems, particularly for entity-specific or time-sensitive prompts.

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AAAI Conference 2024 System Paper

Enhancing Machine Translation Experiences with Multilingual Knowledge Graphs

Simone Conia
Daniel Lee
Min Li
Umar Farooq Minhas
Yunyao Li

Translating entity names, especially when a literal translation is not correct, poses a significant challenge. Although Machine Translation (MT) systems have achieved impressive results, they still struggle to translate cultural nuances and language-specific context. In this work, we show that the integration of multilingual knowledge graphs into MT systems can address this problem and bring two significant benefits: i) improving the translation of utterances that contain entities by leveraging their human-curated aliases from a multilingual knowledge graph, and, ii) increasing the interpretability of the translation process by providing the user with information from the knowledge graph.

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AAAI Conference 2022 System Paper

InteractEva: A Simulation-Based Evaluation Framework for Interactive AI Systems

Yannis Katsis
Maeda F. Hanafi
Martín Santillán Cooper
Yunyao Li

Evaluating interactive AI (IAI) systems is a challenging task, as their output highly depends on the performed user actions. As a result, developers often depend on limited and mostly qualitative data derived from user testing to improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. InteractEva employs (a) a user simulation backend to test the system against different use cases and user interactions at scale with (b) an interactive frontend allowing developers to perform important quantitative evaluation tasks, including acquiring a performance overview, performing error analysis, and conducting what-if studies. The framework has supported the evaluation and improvement of an industrial IAI text extraction system, results of which will be presented during our demonstration.

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AAAI Conference 2021 System Paper

AutoText: An End-to-End AutoAI Framework for Text

Arunima Chaudhary
Alayt Issak
Kiran Kate
Yannis Katsis
Abel Valente
Dakuo Wang
Alexandre Evfimievski
Sairam Gurajada

Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e. g. , domain experts) can quickly generate performant NLP models with support to both control (e. g. , via specifying constraints) and understand learned models.

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AAAI Conference 2021 System Paper

KAAPA: Knowledge Aware Answers from PDF Analysis

Nicolas Fauceglia
Mustafa Canim
Alfio Gliozzo
Jennifer J Liang
Nancy Xin Ru Wang
Douglas Burdick
Nandana Mihindukulasooriya
Vittorio Castelli

We present KaaPa (Knowledge Aware Answers from Pdf Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KaaPa enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KaaPa can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KaaPa for QA on scientific literature using the COVID-19 Open Research Dataset.

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AAAI Conference 2020 System Paper

PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning

Kun Qian
Poornima Chozhiyath Raman
Yunyao Li
Lucian Popa

Entity name disambiguation is an important task for many text-based AI tasks. Entity names usually have internal semantic structures that are useful for resolving different variations of the same entity. We present, PARTNER, a deep learning-based interactive system for entity name understanding. Powered by effective active learning and weak supervision, PARTNER can learn deep learning-based models for identifying entity name structure with low human effort. PARTNER also allows the user to design complex normalization and variant generation functions without coding skills.

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IJCAI Conference 2020 Conference Paper

SEBF: A Single-Chain based Extension Model of Blockchain for Fintech

Yimu Ji
Weiheng Gu
Fei Chen
Xiaoying Xiao
Jing Sun
Shangdong Liu
Jing He
Yunyao Li

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted or-acle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verifi-cation.

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IJCAI Conference 2017 Conference Paper

Active Learning for Black-Box Semantic Role Labeling with Neural Factors

Chenguang Wang
Laura Chiticariu
Yunyao Li

Active learning is a useful technique for tasks for which unlabeled data is abundant but manual labeling is expensive. One example of such a task is semantic role labeling (SRL), which relies heavily on labels from trained linguistic experts. One challenge in applying active learning algorithms for SRL is that the complete knowledge of the SRL model is often unavailable, against the common assumption that active learning methods are aware of the details of the underlying models. In this paper, we present an active learning framework for black-box SRL models (i. e. , models whose details are unknown). In lieu of a query strategy based on model details, we propose a neural query strategy model that embeds both language and semantic information to automatically learn the query strategy from predictions of an SRL model alone. Our experimental results demonstrate the effectiveness of both this new active learning framework and the neural query strategy model.

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AAAI Conference 2007 Conference Paper

Enabling Domain-Awareness for a Generic Natural Language Interface

Yunyao Li
Huahai Yang

In this paper, we present a learning-based approach for enabling domain-awareness for a generic natural language interface. Our approach automatically acquires domain knowledge from user interactions and incorporates the knowledge learned to improve the generic system. We have embedded our approach in a generic natural language interface and evaluated the extended system against two benchmark datasets. We found that the performance of the original generic system can be substantially improved through automatic domain knowledge extraction and incorporation. We also show that the generic system with domain-awareness enabled by our approach can achieve performance similar to that of previous learning-based domain-specific systems.

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