Papers by Dheeraj Rajagopal
StyLEx: Explaining Style Using Human Lexical Annotations (2023.eacl-main)
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| Challenge: | Large pre-trained language models often learn spurious domain-specific words to make predictions. |
| Approach: | They propose a model that learns from human annotated explanations of stylistic features and jointly predicts them as model explanations. |
| Outcome: | The proposed model can provide human like stylistic lexical explanations without sacrificing performance on in-domain and out-of-domain datasets. |
A Dataset for Tracking Entities in Open Domain Procedural Text (2020.emnlp-main)
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Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
| Challenge: | Existing tasks require only a small set of attributes to track state changes in procedural text. |
| Approach: | They propose a task where given a procedural text as input, the task is to generate a set of state change tuples for each step. |
| Outcome: | The proposed task generates state change tuples from a set of pre-defined attributes for each step and predicts them from an open vocabulary. |
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers (2021.emnlp-main)
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| Challenge: | Existing models that explain text classification predictions are opaque and overfit to spurious artifacts. |
| Approach: | They propose a novel self-explaining model that explains a text classifier’s predictions using phrase-based concepts. |
| Outcome: | The proposed model shows that it is adequate, trustworthy and understandable by human judges compared to existing baselines. |
Simple and Effective Semi-Supervised Question Answering (N18-2)
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| Challenge: | Existing deep learning systems for extractive Question Answering are limited and expensive to construct. |
| Approach: | They propose a semi-supervised QA system where end user specifies a set of documents and only a few labelled examples. |
| Outcome: | The proposed system achieves 50% F1 score on SQuAD and TriviaQA with very little labeled data. |
What-if I ask you to explain: Explaining the effects of perturbations in procedural text (2020.findings-emnlp)
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| Challenge: | QUARTET constructs explanations from paragraphs using procedural text . qartet achieves 18 points better on explanation accuracy compared to strong baselines on a recent process comprehension benchmark. |
| Approach: | They propose a system that constructs explanations from paragraphs by modeling the explanation task as a multitask learning problem. |
| Outcome: | The proposed system achieves 18 points better on explanation accuracy compared to strong baselines on a process comprehension benchmark. |
Steering off Course: Reliability Challenges in Steering Language Models (2025.acl-long)
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| Challenge: | Prior studies have evaluated a few steering methods for language models, leaving gaps in understanding their robustness. |
| Approach: | They examine three steering methods for language models to examine their reliability . they use function vectors, task vectors and DoLa to steer models toward desirable outputs . |
| Outcome: | The proposed methods show that they are not robust enough to handle large models with large parameters. |
Could you give me a hint ? Generating inference graphs for defeasible reasoning (2021.findings-acl)
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| Challenge: | Defeasible reasoning is a mode of reasoning where conclusions can be overturned by taking into account new evidence. |
| Approach: | They propose to automatically generate inference graphs for a defeasible inference task by transfer learning from a related NLP task. |
| Outcome: | The proposed method generates meaningful graphs for a defeasible inference task and human accuracy improves by 20%. |
Conditional set generation using Seq2seq models (2022.emnlp-main)
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| Challenge: | Several NLP tasks are instances of set generation. |
| Approach: | They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality. |
| Outcome: | The proposed method improves performance on four benchmark datasets with no additional annotations. |
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents (2022.lrec-1)
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| Challenge: | Existing methods to understand revisions have failed to provide a deeper understanding of the nature of these edits. |
| Approach: | They propose to use a Wikipedia revision history dataset to train a classifier that achieves a 90% accuracy in identifying edit intent and a distantly-supervised model that generates . |
| Outcome: | The proposed model achieves 90% accuracy in identifying edit intent and a best score of 28 ROUGE. |
How Far Can We Extract Diverse Perspectives from Large Language Models? (2024.emnlp-main)
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| Challenge: | Recent advances of large language models have gained much interest from researchers to exploit their capability of creative generation for data augmentation with less cost and higher diversity. |
| Approach: | They propose a criteria-based prompting technique to extract maximum diversity from LLMs. |
| Outcome: | The proposed method extracts diverse opinions from large language models iteratively. |
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)
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| Challenge: | Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution. |
| Approach: | They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context. |
| Outcome: | The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring. |
StructSum: Summarization via Structured Representations (2021.eacl-main)
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| Challenge: | Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document . |
| Approach: | They propose a framework based on document-level structure induction to address layout bias and lack of transparency in abstractive summarization models. |
| Outcome: | The proposed framework improves coverage of content in the source documents and generates more abstractive summaries by generating more novel n-grams. |
Think about it! Improving defeasible reasoning by first modeling the question scenario. (2021.emnlp-main)
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| Challenge: | Existing literature suggests that a person forms a mental model of the problem scenario before answering questions. |
| Approach: | They propose to have a model first create a graph of relevant influences and leverage that graph as an additional input when answering a defeasible query. |
| Outcome: | The proposed model achieves state-of-the-art on three different defeasible reasoning datasets. |