Papers by Mounica Maddela
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (2023.emnlp-main)
Copied to clipboard
| Challenge: | Traditional human evaluation methods for text simplification often relies on individual, shallow sentence-level ratings, easily affected by the annotator's preference or bias. |
| Approach: | They propose an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. |
| Outcome: | The proposed framework is able to predict sentence- and word-level quality simultaneously and report promising results. |
STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing Text2SQL systems do not support SQL analysts in their primary work of performing complex analytics on specialized databases. |
| Approach: | They propose to decompose STARQA questions using SQL and Python to perform reasoning on specialized relational databases. |
| Outcome: | The proposed approach decomposes the task through a combination of SQL and Python, and achieves better performance on the more difficult questions. |
Code and Named Entity Recognition in StackOverflow (2020.acl-main)
Copied to clipboard
| Challenge: | StackOverflow has 15 million programming related questions written by 8.5 million users . however, there is a lack of fundamental NLP resources and techniques for identifying software-related named entities within natural language sentences. |
| Approach: | They propose a named entity recognition corpus for the computer programming domain with 15,372 sentences annotated with 20 fine-grained entity types. |
| Outcome: | The proposed model improves on 152 million sentences from StackOverflow and achieves 79.10 F-1 score for code and named entity recognition. |
EntSUM: A Data Set for Entity-Centric Extractive Summarization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for controllable summarization fail to generate entity-centric summaries. |
| Approach: | They propose to use a human-annotated data set EntSUM to generate controllable summarization with a focus on named entities as the aspects to control. |
| Outcome: | The proposed data set shows that existing methods fail to generate entity-centric summaries. |
BiSECT: Learning to Split and Rephrase Sentences with Bitexts (2021.emnlp-main)
Copied to clipboard
| Challenge: | Several past efforts have created Split and Rephrase training sets, which consist of long, complex input sentences paired with multiple shorter sentences that preserve the meaning of the input sentence. |
| Approach: | They propose a new dataset and a model for this task by extracting 1-2 sentence alignments from bilingual parallel corpora and using machine translation to convert both sides of the corpus into the same language. |
| Outcome: | The proposed model can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. |
Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders (2022.aacl-short)
Copied to clipboard
| Challenge: | Entity-centric summarization is a type of controllable summarizing that aims to produce a summary specific to a given target entity. |
| Approach: | They propose to recast a sentence selection task as a controllable summarization using a dataset supported by EntSUM. |
| Outcome: | The proposed framework outperforms the current state-of-the-art in the sentence selection task and outperformed the competitive entity-centric Lead 3 heuristic by 1.1 F1. |
Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on text simplification have focused on sentence simplification, but these metrics often underperform on longer texts. |
| Approach: | They propose to adapt existing sentence-level metrics for paragraph- or document-level simplification by incorporating a new approach to the evaluation of text simplification metrics. |
| Outcome: | The proposed approach outperforms existing sentence-level metrics in terms of correlation with human judgment and the sensitivity and robustness of various metrics to different types of errors produced by existing systems. |
Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
Copied to clipboard
| Challenge: | Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles. |
| Approach: | They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity. |
| Outcome: | The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1. |
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for cognitive behavioral therapy lack specific and diverse practice material. |
| Approach: | They propose to use a dataset to generate unhelpful thought patterns . they propose to train and evaluate existing models to generate an abundance of practice material . |
| Outcome: | The proposed model can generate unlimited quantity of practice material and generate suitable reframing proposals with no or minimal additional model training required. |
Multi-task Pairwise Neural Ranking for Hashtag Segmentation (P19-1)
Copied to clipboard
| Challenge: | Hashtags are used to add metadata to textual utterances, but their semantic content is difficult to infer as they often contain multiple tokens joined together. |
| Approach: | They propose to use a dataset of 12,594 hashtags to infer hashtag semantics . they propose to frame the problem as a pairwise ranking problem between candidate segmentations . |
| Outcome: | The proposed methods show 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. |
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification (D18-1)
Copied to clipboard
| Challenge: | Current lexical simplification approaches rely on heuristics and corpus level features that do not align with human judgment. |
| Approach: | They propose a human-rated word-complexity lexicon and a neural readability ranking model that uses human ratings to measure the complexity of any given word or phrase. |
| Outcome: | The proposed model performs better than state-of-the-art models for lexical simplification tasks and evaluation datasets. |
LENS: A Learnable Evaluation Metric for Text Simplification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing metrics for text simplification are based on unitary or outdated models, making them unsuitable for this approach. |
| Approach: | They present a learnable evaluation metric for text simplification using language models . they also introduce a human evaluation framework that rates simplifications from several models a list-wise manner . |
| Outcome: | The proposed model correlates much better with human judgment than existing metrics. |
Controllable Text Simplification with Explicit Paraphrasing (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing text simplification systems rely on deletion and do not paraphrase well. |
| Approach: | They propose a hybrid approach that leverages linguistically-motivated rules for splitting and deletion and couples them with a neural paraphrasing model to produce varied rewriting styles. |
| Outcome: | The proposed model improves paraphrasing capability and paraphrases more often than existing models. |