Papers by Daniel Lee
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Existing methods to generate knowledge graphs are unable to handle non-English textual information. |
| Approach: | They propose a task of automatic Knowledge Graph Completion to bridge the gap between English and non-English textual information. |
| Outcome: | The proposed method bridges the gap between the quantity and quality of textual information between English and non-English languages. |
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
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| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models (2024.findings-acl)
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| Challenge: | Language models (LMs) demonstrate exceptional capabilities on tasks, but are vulnerable to extraction attacks. |
| Approach: | They propose Privacy Protection via Optimal Parameters (POP) which induces the model to forget about some of its training data. |
| Outcome: | The proposed method outperforms the state-of-the-art in retaining LM performance on 9 classification and 4 dialogue benchmarks. |
Open Domain Web Keyphrase Extraction Beyond Language Modeling (D19-1)
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| Challenge: | Recent neural methods for keyphrase extraction are mostly observed in documents originating from the scientific domain. |
| Approach: | They develop a neural keyphrase extraction model that goes beyond language understanding to handle the variations of domain and content quality. |
| Outcome: | The proposed model can handle the variations of domain and content quality without restriction of the domain, quality, nor content of the documents. |
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025.coling-main)
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Zelin Zhou, Simone Conia, Daniel Lee, Min Li, Shenglei Huang, Umar Farooq Minhas, Saloni Potdar, Henry Xiao, Yunyao Li
| Challenge: | Existing studies have shown that combining information from KGs in different languages aids knowledge Graph Completion and Knowledge Graph Enhancement. |
| Approach: | They propose a sequence-to-sequence framework that unifies tasks of textual and relational information completion for multilingual knowledge graphs. |
| Outcome: | The proposed framework unifies tasks of KGC and KGE into a single framework. |
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context (2022.findings-naacl)
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| Challenge: | Current benchmarks do not evaluate numeracy of pretraining language models on measurements. |
| Approach: | They propose a new task where a model learns to reconstruct a number with its associated unit given masked text. |
| Outcome: | The proposed model significantly underperforms pre-trained model with baselines and ablations. |
VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation (2021.acl-long)
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Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux
| Challenge: | VoxPopuli provides 400K hours of unlabeled speech data in 23 languages . large amounts of multilingual audio data are needed to achieve similar progress for multilingual ASR and ST. |
| Approach: | They propose a large-scale multilingual corpus that provides 400K hours of unlabeled speech data in 23 languages. |
| Outcome: | The proposed corpus provides 400K hours of unlabeled speech data in 23 languages and 1.8K hours transcribed speeches in 15 languages and their aligned oral interpretations into 15 target languages totaling 17.3K hours. |
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (D19-1)
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| Challenge: | End-to-end reading comprehension models have been successful at extracting text answers, but there are still problems with generalizing them to abstractive numerical reasoning. |
| Approach: | They propose to augment a BERT-based reading comprehension model with a set of executable ‘programs’ which encompass simple arithmetic as well as extraction. |
| Outcome: | The proposed model can perform 33% absolute improvement on the DROP dataset, with very few training examples. |
RETAIN: Interactive Tool for Regression Testing Guided LLM Migration (2024.emnlp-demo)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into diverse applications. |
| Approach: | They propose a tool specifically designed for regression testing during LLM migrations. |
| Outcome: | RETAIN (REgression Testing guided LLM migrAtIoN) provides a tool specifically designed for regression testing during LLM migrations. |
What does the Failure to Reason with “Respectively” in Zero/Few-Shot Settings Tell Us about Language Models? (2023.acl-long)
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| Challenge: | In the context of natural language inference, we examine how language models reason with respective readings from two perspectives: syntactic-semantic and commonsense-world knowledge. |
| Approach: | They propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResLI to encompass various explicit and implicit realizations of "respectively". |
| Outcome: | The proposed datasets include explicit and implicit readings of "respectively" the proposed dataset shows that fine-tuned models struggle with understanding readings without explicit supervision. |
BehaviorSFT: Behavioral Token Conditioning for Health Agents Across the Proactivity Spectrum (2025.findings-emnlp)
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Yubin Kim, Zhiyuan Hu, Hyewon Jeong, Eugene W Park, Shuyue Stella Li, Chanwoo Park, Shiyun Xiong, MingYu Lu, Hyeonhoon Lee, Xin Liu, Daniel McDuff, Cynthia Breazeal, Samir Tulebaev, Hae Won Park
| Challenge: | Large Language Models (LLMs) struggle with proactive engagement, authors say . a blind clinical evaluation confirmed that trained agents exhibit more realistic clinical behavior . |
| Approach: | They propose a training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection. |
| Outcome: | The proposed training strategy boosts performance on both benchmarks. |
ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models (2024.emnlp-industry)
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Ronak Pradeep, Daniel Lee, Ali Mousavi, Jeffrey Pound, Yisi Sang, Jimmy Lin, Ihab Ilyas, Saloni Potdar, Mostafa Arefiyan, Yunyao Li
| Challenge: | Knowledge Graphs (KGs) are a powerful tool for capturing structured representations of the world. |
| Approach: | They propose a scalable method for generating up-to-date and configurable conversational KGQA datasets that adheres to human interaction configurations and operates at a significantly larger scale. |
| Outcome: | Qualitative psychometric analyses show that ConvKGYarn produces high-quality data comparable to popular conversational KGQA datasets across various metrics. |
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport (2025.acl-long)
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| Challenge: | Instruction-following large language models (LLMs) inadvertently disclose private, sensitive information to their users, underscoring the need for machine unlearning techniques to remove selective information from the models. |
| Approach: | They propose an optimal transport-based unlearning method that utilizes the Wasserstein distance from the model’s initial parameters to achieve more effective and fine-grained unlearning. |
| Outcome: | The proposed method surpasses existing methods and establishes a new standard for secure and adaptable LLMs that can accommodate user data removal requests without the need for full retraining. |
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (D18-1)
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| Challenge: | Existing methods for cross-domain sentiment classification are difficult and costly . domain adaptation is difficult because data in source and target domains are drawn from different distributions. |
| Approach: | They propose a semi-supervised learning approach that minimizes the distance between source and target instances in embedded feature space. |
| Outcome: | The proposed approach can improve on baseline methods in various settings. |
Exploiting Document Knowledge for Aspect-level Sentiment Classification (P18-2)
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| Challenge: | Existing public aspect-level datasets for aspect-based sentiment classification are small . existing methods for aspect level sentiment classification require annotation of all opinion targets . |
| Approach: | They propose two approaches that transfer knowledge from document-level data to improve aspect-level sentiment classification. |
| Outcome: | The proposed methods improve aspect-level sentiment classification on 4 public datasets. |
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2025.coling-demos)
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| Challenge: | Recent advances in retrieval-augmented generation have demonstrated impressive performance on the question-answering task. |
| Approach: | They propose a retrieval-augmented generation framework that generates an initial text answer and retrieves multimodal data relevant to the snippets of the initial text. |
| Outcome: | The proposed framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications. |
Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization (2020.coling-main)
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Dongyub Lee, Myeong Cheol Shin, Taesun Whang, Seungwoo Cho, Byeongil Ko, Daniel Lee, EungGyun Kim, Jaechoon Jo
| Challenge: | Existing methods for text summarization are based on recall-oriented understudy for gisting evaluation (ROUGE) scores do not reflect semantic meaning correspondences between generated and reference summaries. |
| Approach: | They propose to use Korean as a summarization language to generate a shorter form of text from the source document preserving salient information. |
| Outcome: | The proposed evaluation metrics improve the correlation between the metrics and human judgment. |
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models (2024.emnlp-main)
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Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel Weld, Joseph Chee Chang, Kyle Lo
| Challenge: | Using language models (LMs) can generate literature review tables by decomposing it into separate schema and value generation steps. |
| Approach: | They propose a framework that leverages language models to perform literature review table generation by decomposing it into separate schema and value generation steps. |
| Outcome: | The proposed framework decomposes the task into two sub-tasks: schema generation and value generation. |
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms (2021.emnlp-main)
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| Challenge: | Pre-trained language models have impressive performance on commonsense inference benchmarks, but their ability to make robust inferences is debated. |
| Approach: | They propose a challenge that evaluates robust commonsense inference despite textual perturbations using commonsensical knowledge bases and probe PTLMs across two different evaluation settings. |
| Outcome: | The proposed procedure evaluates robust commonsense inference despite textual perturbations using commonsensense knowledge bases and probe PTLMs across two evaluation settings. |
DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation (2023.findings-acl)
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| Challenge: | Recent studies suggest that neural classifiers make overly confident predictions for examples from unseen distributions. |
| Approach: | They propose a new evaluation metric, DENSITY, which measures how likely a response would appear in the distribution of human conversations. |
| Outcome: | The proposed metric measures how likely a response would appear in the distribution of human conversations. |
Effective Attention Modeling for Aspect-Level Sentiment Classification (C18-1)
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| Challenge: | Aspect-level sentiment classification aims to determine sentiment polarity of review sentence towards opinion target . main challenge is to separate different opinion contexts for different targets . |
| Approach: | They propose a method that captures the semantic meaning of the opinion target and a model that incorporates syntactic information into the attention mechanism. |
| Outcome: | The proposed method captures the semantic meaning of the opinion target and incorporates syntactic information into the attention mechanism. |
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis (P19-1)
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| Challenge: | Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a sentence. |
| Approach: | They propose an interactive multi-task learning network which can learn multiple tasks simultaneously . they use a shared set of latent variables to iteratively pass information between tasks . |
| Outcome: | The proposed method outperforms existing methods on three benchmark datasets. |