Papers by Robert Vacareanu
Active Learning Design Choices for NER with Transformers (2024.lrec-main)
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Robert Vacareanu, Enrique Noriega-Atala, Gus Hahn-Powell, Marco A. Valenzuela-Escarcega, Mihai Surdeanu
| Challenge: | In the field of natural language processing, active learning is a technique that is used to decide which examples are worth annotating . a number of studies have focused on sequence classification, text classification, question answering, and question answering. |
| Approach: | They propose two different approaches to deal with partially-annotated sentences . they propose an annotation scheme that can be used to train with tokens . |
| Outcome: | The proposed approaches achieve comparable or better performance than sentence-level annotations with a smaller number of annotated tokens. |
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)
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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan
| Challenge: | Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment. |
| Approach: | They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task. |
MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing (2025.findings-naacl)
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| Challenge: | Existing models fail to capture important semantic features of logic such as monotonicity and negation. |
| Approach: | They propose a modular step-by-step approach to natural language inference . they use a language model to generate edits to incrementally transform the premise into the hypothesis . |
| Outcome: | The proposed method outperforms baseline models in realistic cross-domain settings with improvements up to 12.6% (relative). |
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction (2022.lrec-1)
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Robert Vacareanu, Marco A. Valenzuela-Escárcega, George Caique Gouveia Barbosa, Rebecca Sharp, Gustave Hahn-Powell, Mihai Surdeanu
| Challenge: | a "deep learning tsunami" has brought tremendous improvements in performance to most NLP applications. |
| Approach: | They propose a method for rule synthesis from examples that combines the advantages of deep learning and rule-based methods. |
| Outcome: | The proposed method achieves state-of-the-art on 1-shot task and competitive performance in 5-shot scenario. |
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction (2022.naacl-demo)
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Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
| Challenge: | Vacareanu et al., 2021) proposes a system that helps users build transparent information extraction models . rule-based methods address the opacity of neural architectures by producing models that are transparent . |
| Approach: | They propose a system that assists a user in constructing transparent information extraction models . the system generates high-precision rules even in a 1-shot setting, they show . |
| Outcome: | The proposed system generates high-precision rules even in a 1-shot setting . it outperforms manually written patterns on a widely-used relation extraction dataset . |
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)
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| Challenge: | Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required. |
| Approach: | They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset. |
| Outcome: | The proposed methods perform poorly on the few-shot relation extraction task. |
Parsing as Tagging (2020.lrec-1)
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| Challenge: | Existing methods for dependency parsing treat parse as tagging, but they are not perfect. |
| Approach: | They propose a simple yet accurate method that treats parsing as tagging . they use a sequence model with a bidirectional LSTM over BERT embeddings . |
| Outcome: | The proposed method outperforms the state-of-the-art method on universal dependency (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS in German. |
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification (2024.findings-naacl)
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| Challenge: | a novel neuro-symbolic architecture for relation classification combines rule-based methods with deep learning techniques. |
| Approach: | They propose a neuro-symbolic architecture for relation classification that combines rule-based methods with deep learning techniques. |
| Outcome: | The proposed approach outperforms state-of-the-art models in three out of four settings . human interventions boost the performance on the relation org:parents by as much as 26% relative improvement . |
When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context (2024.findings-emnlp)
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Enrique Noriega-Atala, Robert Vacareanu, Salena Ashton, Adarsh Pyarelal, Clayton Morrison, Mihai Surdeanu
| Challenge: | a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurately predict the relevant scenario information. |
| Approach: | They propose a neural architecture finetuned for the task of scenario context generation . they use a curated dataset of time and location annotations to train an encoder-decoder architecture . |
| Outcome: | The proposed model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurately predict the relevant scenario information of a particular entity or event. |
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification (2020.coling-main)
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| Challenge: | Existing methods for learning multi-word expressions have language sparsity and are not supervised. |
| Approach: | They propose an unsupervised approach to learning a compositional representation function for multi-word expressions . they use a Tratz dataset to train the composition function on the word-semantic relation . |
| Outcome: | The proposed method outperforms the previous state-of-the-art method on the Tratz dataset with an F1 score of 50.4%. |