Papers by Katrin Erk
Modeling Semantic Plausibility by Injecting World Knowledge (N18-2)
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| Challenge: | Existing models for semantic plausibility are based on distributional data, but injecting knowledge about entity properties provides a substantial performance boost. |
| Approach: | They propose to inject manually elicited knowledge about entity properties into a dataset to improve plausibility models. |
| Outcome: | The proposed dataset is a great testbed for semantic plausibility models . it shows that injection of knowledge about entity properties improves performance . |
Picking Apart Story Salads (D18-1)
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| Challenge: | Story salads are mixtures of multiple documents that can be generated at scale . they exhibit challenging inference problems, and require global context and coherence . |
| Approach: | They propose to generate salads that exhibit challenging inference problems by exploiting the Wikipedia hierarchy . they propose a task where the objective is to group sentences from the same narratives . |
| Outcome: | The proposed task is based on a novel, challenging clustering task using Wikipedia . it is difficult to identify relevant information and assemble it into coherent narratives . |
Query-focused Scenario Construction (D19-1)
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| Challenge: | Stronger neural network models and harder synthetic training settings are important to achieve high performance. |
| Approach: | They propose a query-based system that extracts compatible sets of events from news data . stronger neural network models and harder synthetic training settings are important to achieve high performance . |
| Outcome: | The proposed system outperforms baselines on a human-curated dataset of scenarios about real-world news topics. |
Help! Need Advice on Identifying Advice (2020.emnlp-main)
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| Challenge: | Pre-trained systems are able to capture advice better than rule-based systems, but advice identification is challenging. |
| Approach: | They analyze a dataset of advice posts on two reddit forums and annotate whether they contain advice. |
| Outcome: | The proposed models show that pre-trained models capture advice better than rule-based systems, but advice identification is challenging. |
Did they answer? Subjective acts and intents in conversational discourse (2021.naacl-main)
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| Challenge: | Discourse signals are often implicit, leaving it up to the interpreter to draw inferences . current discourse data and frameworks ignore the social aspect, expecting only a single ground truth . elisa f. and her team present a dataset with multiple and subjective interpretations of English conversation . |
| Approach: | They present a first discourse dataset with multiple and subjective interpretations of English conversation . they show disagreements are nuanced and require a deeper understanding of contextual factors . |
| Outcome: | The proposed dataset shows disagreements are nuanced and require deeper understanding of contextual factors. |
Evaluating Discourse in Structured Text Representations (P19-1)
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| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |
SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events (2024.findings-acl)
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| Challenge: | a recent study suggests that understanding goal-driven actions is challenging because of the small variations in the narrative that lead to vastly different goals, achievement outcomes and future actions. |
| Approach: | They propose to use a participant achievement lens to interpret and assess goal driven actions . they collect 6.3K high quality goal and action annotations reflecting their proposed lens . |
| Outcome: | The proposed lens can be used to interpret and assess goal driven actions . it can be fine-tuned on the dataset to achieve performance surpassing larger models . |
To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation (2024.lrec-main)
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| Challenge: | State-of-the-art language models perform well on a variety of language tasks, but struggle with understanding negation cues in tasks like natural language inference (NLI). |
| Approach: | They propose a new learning strategy for negation building on ELECTRA’s replaced token detection objective. |
| Outcome: | The proposed approach leads to substantial gains on a variant of RTE with additional negation. |
Leveraging WordNet Paths for Neural Hypernym Prediction (2020.coling-main)
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| Challenge: | Existing work on lexical relations based on distributed representations has differed widely. |
| Approach: | They propose a model that generates taxonomy paths for hypernym prediction using WordNet sequences. |
| Outcome: | The hypo2path model outperforms the best model by 4.11 points in hit-at-one (H@1) The proposed model outpersforms previous models by a factor of 0.9. |
Implicit Argument Prediction with Event Knowledge (N18-1)
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| Challenge: | Existing work on identifying implicit arguments has been limited due to large number of features and small datasets . a neural model that uses narrative coherence and entity salience is used to train implicit arguments . |
| Approach: | They propose to train models for implicit argument prediction on a simple cloze task . they use narrative coherence and entity salience to build a neural model . |
| Outcome: | The proposed model performs better on synthetic and natural data. |
Adjusting Interpretable Dimensions in Embedding Space with Human Judgments (2024.naacl-long)
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| Challenge: | Embedding spaces contain interpretable dimensions indicating gender, formality in style, or even object properties. |
| Approach: | They combine seed-based vectors with human ratings of where words fall along a specific dimension to evaluate on predicting object properties and stylistic properties. |
| Outcome: | The proposed model improves on seed-based vectors and human ratings on object properties and stylistic properties. |
SAGEViz: SchemA GEneration and Visualization (2023.emnlp-demo)
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Sugam Devare, Mahnaz Koupaee, Gautham Gunapati, Sayontan Ghosh, Sai Vallurupalli, Yash Kumar Lal, Francis Ferraro, Nathanael Chambers, Greg Durrett, Raymond Mooney, Katrin Erk, Niranjan Balasubramanian
| Challenge: | Schema induction involves creating a graph representation depicting how events unfold . supervised and few-shot approaches are not scalable and time-consuming . |
| Approach: | They propose a tool that utilizes human-AI collaboration to create and update complex schema graphs efficiently. |
| Outcome: | The proposed tool can generate schemas of better quality and be used by users in a variety of domains. |
Deep Neural Models of Semantic Shift (N18-1)
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| Challenge: | Diachronic distributional models track changes in word use over time using a continuous variable and a synthetic task to measure the semantic trajectory of a word. |
| Approach: | They propose a deep neural network diachronic distributional model that represents time as a continuous variable and model a word’s usage as . a synthetic task which measures how well a model captures the semantic trajectory of a . word over time. |
| Outcome: | The proposed model can capture the semantic trajectory of a word over time and can measure the speed of lexical change. |
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events (2022.emnlp-main)
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| Challenge: | Existing language models lag behind human performance in subtle ways in understanding complex situations, e.g., if the Argentine government yields to [IMF] pressure to rescind emergency legislation meant to protect ordinary families like the Brofmans. |
| Approach: | They propose to pre-identify a participant in a complex event and annotate their volitional engagement in causing the situation. |
| Outcome: | The proposed model can be used to infer the collective impact of salient events that make up a complex event, annotate volitional engagement of participants, and ground the outcome in state changes of the participants. |
X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs (2024.naacl-long)
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| Challenge: | X-parade is the first cross-lingual dataset of paragraph-level information divergences and entailments . ability to recognize differences in meaning underlies many NLP tasks . |
| Approach: | They propose a cross-lingual dataset of paragraph-level information divergences . they use a dictionary to identify new or inferred information in Wikipedia pages . |
| Outcome: | The proposed dataset shows that the proposed methods fail to handle inferable information . the dataset contains fine-grained span-level annotations for content in different languages . |
A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces (2023.acl-long)
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| Challenge: | Existing paradigms for the linguistically oriented exploration of large neural language models include treating the model as a linguistic test subject by measuring output on test sentences and building probing classifiers on top of embeddings to test whether the embeddables are sensitive to certain properties like dependency structure. |
| Approach: | They project contextual embeddings into interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. |
| Outcome: | The proposed method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes. |