Papers by Lillian Lee
Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction (2021.acl-long)
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| Challenge: | Existing bubble representations encoding coordination boundaries and internal relationships are difficult to detect and parse . |
| Approach: | They propose a bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. |
| Outcome: | The proposed bubble parser beats state-of-the-art approaches on coordination structure prediction . the proposed system is based on a GENIA corpus and a Penn treebank . |
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers (N18-2)
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| Challenge: | Non-projective dependency trees account for 12.59% of all training sentences in the annotated Universal Dependencies (UD) 2.1 data. |
| Approach: | They generalize Cohen et al.'s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. |
| Outcome: | The proposed system can be extended to include a variant that reduces time complexity to O(n6), improving over the known bounds in exact inference for non-projective transition-based parsing. |
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (N19-1)
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| Challenge: | Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts. |
| Approach: | They analyze reddit.com data to predict the ultimate controversiality of posts . they use textual content and tree structure of early comments to predict content . |
| Outcome: | The proposed model predicts the ultimate controversiality of posts using features drawn from textual content and tree structure of early comments. |
Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations (2025.acl-long)
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| Challenge: | During a conversation, there can come certain moments where its outcome hangs in the balance. |
| Approach: | They propose an unsupervised computational method for detecting pivotal moments as they happen. |
| Outcome: | The proposed method aligns with human perception and the eventual conversational trajectory, which is more likely to change course at these moments. |
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (N18-1)
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| Challenge: | Existing work suggests that concepts with concrete visual manifestations are easier to learn than abstract ones. |
| Approach: | They propose an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. |
| Outcome: | The proposed algorithm predicts the capacity of machine learning algorithms to learn textual/visual relationships. |
Global Transition-based Non-projective Dependency Parsing (P18-1)
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| Challenge: | Until recently, transition-based dependency parsers were limited to approximate inference due to their incompatibility with rich feature models. |
| Approach: | They propose a transition-based parser with high coverage on non-projective treebanks to support non- projective parsing. |
| Outcome: | The proposed approach is more efficient than its projective counterpart in non-projective languages. |
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest (2023.acl-long)
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Jack Hessel, Ana Marasovic, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi
| Challenge: | Large neural networks can generate jokes, but do they really “understand” humor? a new challenge challenges AI models to match a joke to a cartoon, identify a winning caption, and explain why a winner is funny. |
| Approach: | They propose three tasks based on the New Yorker Cartoon Caption Contest . they aim to match a joke to a cartoon, identify a winning caption and explain why it's funny . |
| Outcome: | The proposed tasks are based on the New Yorker Cartoon Caption Contest . they include matching a joke to a cartoon, identifying a winning caption, and explaining why a funny caption is funny. |
Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents (D19-1)
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| Challenge: | a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific images and specific sentences. |
| Approach: | They propose algorithms that discover image-sentence relationships without explicit annotation . they experiment on seven datasets of varying difficulty . |
| Outcome: | The proposed algorithms can predict links between images and sentences without explicit multimodal annotation. |
Learning Syntax from Naturally-Occurring Bracketings (2021.naacl-main)
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| Challenge: | a new method for learning naturally-occurring bracketings is developed . it uses noisy and incomplete data to induce syntactic structures . |
| Approach: | They propose a partial-brackets-aware structured ramp loss in learning to address this challenge . they show that distantly-supervised models trained on naturally-occurring bracketing data are more accurate . constituency is a foundational building block for phrase-structure grammars, they argue . |
| Outcome: | The proposed model achieves an unlabeled F1 score for constituency parsing on the English WSJ corpus. |
Taking a turn for the better: Conversation redirection throughout the course of mental-health therapy (2024.findings-emnlp)
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| Challenge: | Mental-health therapy involves a complex conversation flow in which patients and therapists continuously negotiate what should be talked about next. |
| Approach: | They propose a measure to quantify the extent to which a certain utterance immediately redirects the flow of the conversation in a large, widely-used online therapy platform. |
| Outcome: | The proposed measure measures the extent to which a certain utterance immediately redirects the flow of the conversation over multiple sessions in a large, widely-used online therapy platform. |
Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild (2025.emnlp-main)
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| Challenge: | Existing methods for lexical semantic-change detection quantify changes in the meaning of words over time. |
| Approach: | They propose to use a top-k setup to evaluate semantic-change discovery despite lacking complete annotations on a battery of semantic-changing detection methods. |
| Outcome: | The proposed setup extends the annotations in the commonly used LiverpoolFC and SemEval-EN benchmarks by 85% and 90%. |
Valency-Augmented Dependency Parsing (D18-1)
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| Challenge: | valency analysis is a complex task that requires a large number of subcategorizations, such as the number and types of syntactic dependents. |
| Approach: | They propose a parsing approach that explicitly models the number and types of syntactic dependents as valency patterns and a probabilistic model for tagging them. |
| Outcome: | The proposed approach outperforms the state-of-the-art labeled attachment score on 53 treebanks representing 41 languages and outperformed the previous state- of-the art labeles by 0.7. |
Does my multimodal model learn cross-modal interactions? It’s harder to tell than you might think! (2020.emnlp-main)
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| Challenge: | a new tool for evaluating expressive cross-modal interactions is needed . empirical multimodally-additive function projection is a tool for isolating unimodal structure . |
| Approach: | They propose a tool that modifies model predictions so that cross-modal interactions are eliminated . they propose to use EMAP to evaluate models' ability to leverage cross-module interactions . |
| Outcome: | The proposed tool can be used to evaluate models on image+text classification tasks . it finds that removing cross-modal interactions results in little to no performance degradation . |
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries (2020.findings-emnlp)
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| Challenge: | Large-scale semantic parsing datasets annotated with logical forms have enabled advances in supervised approaches. |
| Approach: | They propose to enrich English-language questions with SQL equivalents and alignments . they propose to use supervised attention and an auxiliary objective to disambiguate references . |
| Outcome: | The proposed method improves over strong baselines by 4.4% execution accuracy. |
Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches (2020.acl-main)
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| Challenge: | Headless multi-word expressions are frequent in natural language but lack internal syntactic dominance relations. |
| Approach: | They propose an efficient joint decoding algorithm that combines scores from both strategies. |
| Outcome: | The proposed algorithm combines scores from parsing and tagging for predicting flat MWEs . the proposed algorithm is more accurate than parse and more efficient for non-BERT features . |