Papers by Paramita Mirza
Discovering the Functions of Language in Online Forums (D19-55)
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| Challenge: | a vast amount of work has been dedicated to speech act categorization for characterizing discourses . lack of formalism and diversity of taxonomies make it difficult to compare different annotated datasets. |
| Approach: | They propose a semi-supervised framework for predicting the functions of Reddit comments . they propose to use the framework to analyze online forum conversations . |
| Outcome: | The proposed framework can predict functions of Reddit comments and 165K comments. |
Stratified Selective Sampling for Instruction Tuning with Dedicated Scoring Strategy (2025.findings-emnlp)
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| Challenge: | Recent work shows that post-training datasets can be substantially downsampled without noticeably deteriorating performance. |
| Approach: | They propose a method that efficiently bins data into groups and scores difficulty using specialized models. |
| Outcome: | The proposed method can be efficient and universally applied to post-training datasets. |
PRIDE: Predicting Relationships in Conversations (2021.emnlp-main)
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| Challenge: | Existing methods for extracting interpersonal relationships from dialogues are limited to end-to-end learning. |
| Approach: | They propose a neural multi-label classifier that infers relationships from dialogues by external knowledge about speaker features and conversation style. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on large-scale datasets with directed relationships of conversation participants. |
AligNarr: Aligning Narratives on Movies (2021.acl-short)
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| Challenge: | Experimental results show the viability of an unsupervised approach to align movie scripts with plot summaries. |
| Approach: | They propose an unsupervised method to align movie scripts with plot summaries using a global optimization model. |
| Outcome: | The proposed method outperforms a baseline alignment model on ten movies with 76% F1 score. |
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population (D19-1)
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| Challenge: | KnowledgeNet provides text exhaustively annotated with facts . high-quality KBs still rely almost exclusively on human-curated structured or semi-structured data. |
| Approach: | They propose five baseline approaches to populating a knowledge base with facts . the best approach achieves an F1 score of 0.50, significantly outperforming a traditional approach by 79% . |
| Outcome: | The best approach achieves an F1 score of 0.50, outperforming a traditional approach by 79%, indicating the dataset is challenging. |
RedDust: a Large Reusable Dataset of Reddit User Traits (2020.lrec-1)
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| Challenge: | Social media is a rich source of assertions about personal traits, but identifying personal traits from implicit assertions is difficult because of the users’ highly varied vocabulary and expressions. |
| Approach: | They propose to build a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age, and gender. |
| Outcome: | The proposed resource is the first annotated language resource about Reddit users at large scale. |
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler (2024.lrec-main)
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| Challenge: | State-of-the-art intent classification and slot filling methods rely on data-intensive deep learning models . large language models exhibit remarkable zero-shot performance across various natural language tasks. |
| Approach: | They propose an approach framing IC and SF as language generation tasks for instruction-LLMs with a more efficient SF-prompting method. |
| Outcome: | The proposed approach outperforms state-of-the-art IC+SF method and in-context learning methods with GPT3.5 (175B). |
CHARM: Inferring Personal Attributes from Conversations (2020.emnlp-main)
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| Challenge: | Personal Knowledge Bases (PKBs) capture individual user traits for customizing downstream applications like chatbots or recommenders. |
| Approach: | They propose a method that leverages keyword extraction and document retrieval to predict attribute values that were never seen during training. |
| Outcome: | The proposed method can predict attributes that were never seen during training. |
Coverage of Information Extraction from Sentences and Paragraphs (D19-1)
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| Challenge: | Textual information extraction (IE) uses textual features to negate stronger statements, such as the negation of stronger statements. |
| Approach: | They propose to use textual features to predict whether a given text segment mentions all objects standing in a certain relationship with a subject. |
| Outcome: | The proposed features can predict whether a given text segment mentions all objects standing in a certain relationship with a particular subject. |