Search Results for author: Aparna Garimella

Found 26 papers, 5 papers with code

AUTOSUMM: Automatic Model Creation for Text Summarization

no code implementations EMNLP 2021 Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella

Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets.

Abstractive Text Summarization Knowledge Distillation +2

Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation

no code implementations INLG (ACL) 2020 Emily Saldanha, Aparna Garimella, Svitlana Volkova

We perform multi-dimensional evaluation of model performance on mimicking both the style and linguistic differences that distinguish news of different credibility using machine translation metrics and classification models.

Machine Translation Style Transfer +1

Towards Region-aware Bias Evaluation Metrics

no code implementations23 Jun 2024 Angana Borah, Aparna Garimella, Rada Mihalcea

Our proposed approach uses gender-aligned topics for a given region and identifies gender bias dimensions in the form of topic pairs that are likely to capture gender societal biases.

Is this a bad table? A Closer Look at the Evaluation of Table Generation from Text

no code implementations21 Jun 2024 Pritika Ramu, Aparna Garimella, Sambaran Bandyopadhyay

Understanding whether a generated table is of good quality is important to be able to use it in creating or editing documents using automatic methods.

FABLES: Evaluating faithfulness and content selection in book-length summarization

3 code implementations1 Apr 2024 Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, Mohit Iyyer

While LLM-based auto-raters have proven reliable for factuality and coherence in other settings, we implement several LLM raters of faithfulness and find that none correlates strongly with human annotations, especially with regard to detecting unfaithful claims.

Long-Context Understanding

"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

1 code implementation13 Oct 2023 Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, Nanyun Peng

Through benchmarking evaluation on 2 popular LLMs- ChatGPT and Alpaca, we reveal significant gender biases in LLM-generated recommendation letters.

Benchmarking Fairness +1

KNN-LM Does Not Improve Open-ended Text Generation

no code implementations24 May 2023 Shufan Wang, Yixiao Song, Andrew Drozdov, Aparna Garimella, Varun Manjunatha, Mohit Iyyer

Digging deeper, we find that interpolating with a retrieval distribution actually increases perplexity compared to a baseline Transformer LM for the majority of tokens in the WikiText-103 test set, even though the overall perplexity is lower due to a smaller number of tokens for which perplexity dramatically decreases after interpolation.

Retrieval Text Generation

Investigating Strategies for Clause Recommendation

1 code implementation21 Jan 2023 Sagar Joshi, Sumanth Balaji, Jerrin Thomas, Aparna Garimella, Vasudeva Varma

Clause recommendation is the problem of recommending a clause to a legal contract, given the context of the contract in question and the clause type to which the clause should belong.

Text Generation

Graph-based Keyword Planning for Legal Clause Generation from Topics

1 code implementation7 Jan 2023 Sagar Joshi, Sumanth Balaji, Aparna Garimella, Vasudeva Varma

Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation.

Text Generation

What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions

no code implementations19 Dec 2022 Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger

In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties.

Extractive Summarization Sentence +1

Agent-Specific Deontic Modality Detection in Legal Language

no code implementations23 Nov 2022 Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger

Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties.

Diversity Natural Language Understanding +1

CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring

no code implementations EMNLP 2021 Vinay Aggarwal, Aparna Garimella, Balaji Vasan Srinivasan, Anandhavelu N, Rajiv Jain

We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context.

EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction

no code implementations EACL 2021 Bhanu Prakash Reddy Guda, Aparna Garimella, Niyati Chhaya

Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users' language preferences.

BIG-bench Machine Learning

DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting

no code implementations EACL 2021 Hrituraj Singh, Gaurav Verma, Aparna Garimella, Balaji Vasan Srinivasan

In this paper, we propose a Director-Generator framework to rewrite content in the target author's style, specifically focusing on certain target attributes.

Denoising Language Modelling

Women's Syntactic Resilience and Men's Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

no code implementations ACL 2019 Aparna Garimella, Carmen Banea, Dirk Hovy, Rada Mihalcea

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences.

Dependency Parsing Part-Of-Speech Tagging

Demographic-aware word associations

no code implementations EMNLP 2017 Aparna Garimella, Carmen Banea, Rada Mihalcea

Variations of word associations across different groups of people can provide insights into people{'}s psychologies and their world views.

Information Retrieval Keyword Extraction +1

Zooming in on Gender Differences in Social Media

no code implementations WS 2016 Aparna Garimella, Rada Mihalcea

Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe.

General Classification Sociology +3

Identifying Cross-Cultural Differences in Word Usage

no code implementations COLING 2016 Aparna Garimella, Rada Mihalcea, James Pennebaker

Personal writings have inspired researchers in the fields of linguistics and psychology to study the relationship between language and culture to better understand the psychology of people across different cultures.

Cultural Vocal Bursts Intensity Prediction

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