Search Results for author:
Found 22 papers, 5 papers with code
Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation
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.
AUTOSUMM: Automatic Model Creation for Text Summarization
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.
FABLES: Evaluating faithfulness and content selection in book-length summarization
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.
IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context
We filter and translate the existing CrowS-Pairs dataset to create a benchmark dataset suited to the Indian context in Hindi language.
"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters
Through benchmarking evaluation on 2 popular LLMs- ChatGPT and Alpaca, we reveal significant gender biases in LLM-generated recommendation letters.
KNN-LM Does Not Improve Open-ended Text Generation
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.
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Learning template based information extraction from documents is a crucial yet difficult task.
Investigating Strategies for Clause Recommendation
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.
Graph-based Keyword Planning for Legal Clause Generation from Topics
Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation.
What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
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.
Agent-Specific Deontic Modality Detection in Legal Language
Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties.
CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring
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
Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users' language preferences.
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
In this paper, we propose a Director-Generator framework to rewrite content in the target author's style, specifically focusing on certain target attributes.
``Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework
The subjective nature of humor makes computerized humor generation a challenging task.
"Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework
The subjective nature of humor makes computerized humor generation a challenging task.
Women's Syntactic Resilience and Men's Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing
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.
Demographic-aware word associations
Variations of word associations across different groups of people can provide insights into people{'}s psychologies and their world views.
Zooming in on Gender Differences in Social Media
Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe.
Identifying Cross-Cultural Differences in Word Usage
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.
