Papers by Monica Lam
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
| Approach: | They propose a zero-shot transfer learning technique where training data are synthesized from an abstract dialogue model and the ontology of the domain. |
| Outcome: | The proposed technique improves the state of the art on the multi-domain dialogue state tracking dataset by 21%. |
Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations (2024.emnlp-main)
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| Challenge: | Recent advances in language models (LMs) and retrieval-augmented generation (RAG) have led to more capable chatbots and generative search engines. |
| Approach: | They propose to emulate the educational scenario where children/students learn by listening to and participating in conversations of their parents/teachers by watching and steering the discourse among several LM agents. |
| Outcome: | The proposed system outperforms baseline methods on discourse trace and report quality and is preferred by 70% of participants over a search engine and 78% over sabota. |
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation (2022.findings-acl)
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| Challenge: | Existing approaches to build effective semantic parsers for Wizard-of-Oz are insufficient. |
| Approach: | They propose a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations. |
| Outcome: | The proposed model can predict precise dialogue states in WOZ conversations. |
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to write grounded, long-form articles have limited planning capacity and require extensive research and planning in the pre-writing stage. |
| Approach: | They propose a system for the Synthesis of Topic Outlines throughRetrieval and Multi-perspective Question Asking that models the pre-writing stage by (1) discovering diverse perspectives in researching the given topic, (2) simulating conversations where writers carrying different perspectives pose questions to a topic expert grounded on trusted Internet sources, (3) curating the collected information to create an outline. |
| Outcome: | The proposed system is based on a dataset of high-quality Wikipedia articles and evaluates the pre-writing stage. |
SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing (2024.findings-acl)
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| Challenge: | SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes is a hybrid question-answering pipeline . |
| Approach: | They propose a hybrid question-answering pipeline that leverages knowledge from multiple knowledge sources. |
| Outcome: | The proposed approach achieves state-of-the-art on the Compmix dataset with 56.5% exact match rate. |
Grounding Open-Domain Instructions to Automate Web Support Tasks (2021.naacl-main)
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| Challenge: | RUSS is a task and dataset to ground natural language instructions on the web to perform previously unseen tasks. |
| Approach: | They build a task and dataset to ground AI agents from open-domain, step-by-step instructions on the web. |
| Outcome: | The proposed model outperforms existing models that map instructions to actions without WebLang. |
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data (2020.emnlp-main)
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| Challenge: | Existing methods to generate semantic parsers that answer questions on databases require large amounts of annotated data. |
| Approach: | They propose a method to generate semantic parsers that answer questions on databases . they use automatic paraphrasing and template-based parsing to find alternative expressions . |
| Outcome: | The proposed method achieves 69.8% answer accuracy on natural questions, 16.4% higher than state-of-the-art models and 5.2% lower than the same model trained with human data. |
Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets (2025.acl-long)
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| Challenge: | Existing LLMs suffer from hallucination, following instructions with conditional logic, and integrating knowledge from different sources. |
| Approach: | They propose a programmable framework for creating knowledge-intensive task-oriented conversational agents that handle involved interactions and answer complex queries. |
| Outcome: | The proposed framework outperforms SOTA methods on complex logic dialogue datasets by up to 20.5%. |
Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models (2025.emnlp-main)
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Sina Semnani, Jirayu Burapacheep, Arpandeep Khatua, Thanawan Atchariyachanvanit, Zheng Wang, Monica Lam
| Challenge: | a new study examines the accuracy of Wikipedia's factual inconsistencies . a corpus-level inconsistent detection system can help editors identify inconsistances . |
| Approach: | They propose a corpus-level inconsistency detection system that combines LLM reasoning with retrieval to detect and contextualize potential contradictions for human review. |
| Outcome: | The proposed system can detect inconsistencies in Wikipedia and human review. |
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)
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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam
| Challenge: | X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research . |
| Approach: | They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source . |
| Outcome: | The proposed toolkit accelerates the post-editing of a new language dataset after translation. |
CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition (2025.emnlp-main)
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| Challenge: | Existing vision-language models are not equipped to read diverse languages and scripts found in historical materials. |
| Approach: | They propose to train an open-weight vision-language model for historical text recognition on CHURRO-DS, the largest historical text-recognition dataset to date. |
| Outcome: | The proposed model outperforms existing vision-language models on CHURRO-DS, the largest historical text recognition dataset to date. |
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have led to significant improvements in the Knowledge Base Question Answering task. |
| Approach: | They introduce an expert-annotated KBQA dataset from Wikidata’s “Request a Query” forum with 320 decontextualized question-SPARQL pairs. |
| Outcome: | The SPINACH dataset outperforms baselines on the QALD-7, QADL-9 Plus and QAL-10 datasets by 31.0%, 27.0% and 10.0% in F1 respectively. |
WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia (2023.findings-emnlp)
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| Challenge: | a new few-shot LLM-based chatbot is able to provide factual and engaging responses . a novel hybrid human-and-LLM evaluation methodology is used to evaluate the system . |
| Approach: | They propose a few-shot LLM-based chatbot that almost never hallucinates . they distill WikiChat into a 7B-parameter LLaMA model with minimal loss of quality . |
| Outcome: | The proposed system outperforms retrieval-based and LLM-based systems on the Wikipedia corpus. |
Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation (2020.emnlp-main)
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| Challenge: | a new toolkit for localizing a semantic parser for a language is proposed . the proposed approach is based on a method for question answering systems . |
| Approach: | They propose a toolkit that leverages Neural Machine Translation systems to localize a semantic parser for a new language. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in 10 new languages . it can be deployed in restaurants and hotels in less than 24 hours . |
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World (2025.findings-acl)
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Sina Semnani, Pingyue Zhang, Wanyue Zhai, Haozhuo Li, Ryan Beauchamp, Trey Billing, Katayoun Kishi, Manling Li, Monica Lam
| Challenge: | Using a partially reannotated subset of the Armed Conflict Location & Event Data, we analyze 39,786 conflict events across 20 languages and 171 countries. |
| Approach: | They propose a large-scale conflict event dataset with extensive coverage of region-specific entities. |
| Outcome: | The proposed method detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. |
Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata (2023.emnlp-main)
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| Challenge: | Large language models can answer many questions correctly, but can also hallucinate and give wrong answers. |
| Approach: | They propose a question-answering benchmark for Wikidata that uses SPARQL to ground large language models. |
| Outcome: | The proposed method outperforms the state-of-the-art for QALD-7 by 3.6% in F1 score. |
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues (2023.eacl-main)
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| Challenge: | Existing methods for predicting state of a conversation are limited to a few languages . a method that can be applied to other languages will benefit the large population of speakers of many other languages. |
| Approach: | They propose to automatically translate large-scale dialogue data sets in one language to produce an effective semantic parser for other languages using machine translation. |
| Outcome: | The proposed model reduces the compounding effect of translation errors without harming the accuracy in practice. |
Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval (2024.findings-emnlp)
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Kazuaki Furumai, Roberto Legaspi, Julio Romero, Yudai Yamazaki, Yasutaka Nishimura, Sina Semnani, Kazushi Ikeda, Weiyan Shi, Monica Lam
| Challenge: | Existing methods to improve persuasive chatbots use only a handful of predefined strategies. |
| Approach: | They propose a persuasive chatbot based on large language models that is factual and more persuasive by leveraging many more nuanced strategies. |
| Outcome: | The proposed chatbot is factual and more persuasive by leveraging many more nuanced strategies. |
SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models (2024.findings-naacl)
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| Challenge: | SUQL is a conversational language that supports the generality of hybrid data access for large knowledge corpora. |
| Approach: | They propose a conversational agent that supports the full generality of hybrid data access for large knowledge corpora using SUQL. |
| Outcome: | The proposed language can handle hybrid data sources. |
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation (2023.eacl-main)
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| Challenge: | Existing low-cost approaches to build a high-quality functioning dialogue agent are limited to a few widely-spoken languages. |
| Approach: | They propose automatic methods that use ToD training data to build a functioning agent in another language . they compare the method to existing methods that only use a small training set . |
| Outcome: | The proposed method improves the state-of-the-art in Chinese to English transfer using zero-shot data compared to existing full-shot methods . the proposed method achieves 46.7% and 22.0% in task success rate and dialogue success rate, respectively. |