Detecting Context-Dependent Paraphrases in News Interview Dialogs

Core Concepts

Context-dependent paraphrases in dialog, where the second speaker rephrases part of the first speaker's utterance, can be detected using in-context learning and token classification models.

Abstract

The article investigates context-dependent paraphrases in news interview dialogs, where the interviewer (host) paraphrases part of the interviewee's (guest) utterance. The key points are:

Paraphrases in dialog are often context-dependent, meaning they are semantically equivalent only in the given situation, not necessarily in all contexts.
The authors provide an operationalization of context-dependent paraphrases and develop a training for crowd-workers to annotate such paraphrases.
They introduce a dataset of 600 utterance pairs from NPR and CNN news interviews, with 5,581 annotations on paraphrases and their positions.
The authors experiment with in-context learning using large language models like GPT-4 and fine-tuned DeBERTa token classification models to detect context-dependent paraphrases.
The models achieve promising results, with GPT-4 performing best for paraphrase classification and DeBERTa for paraphrase position highlighting.
The dataset exhibits plausible label variation, where annotators may disagree on whether a text pair constitutes a paraphrase, highlighting the contextual nature of the task.

Stats

"We don't really know what went into their algorithm to make it turn out that way."
"In Harrison County."
"If you're not in our country, there are no constitutional protections for you."
"There are militant groups out there firing against the military."

Quotes

"Repeating or paraphrasing what the previous speaker said has time and time again been found to be important in human-to-human or human-to-computer dialogs: It encourages elaboration and introspection in counseling, can help deescalate conflicts in crisis negotiations, can have a positive impact on relationships, can increase the perceived response quality of dialog systems and generally provides tangible understanding-checks to ground what both speakers agree on."
"Even though context-dependent paraphrase identification in dialog might at first seem straight forward with a clear ground truth, similar to other "objective" tasks in NLP, human annotators (plausibly) disagree on labels."

Key Insights Distilled From

What's Mine becomes Yours

by Anna Wegmann... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06670.pdf

Deeper Inquiries

How can the dataset be expanded to include more diverse dialog settings beyond news interviews?

To expand the dataset to include more diverse dialog settings beyond news interviews, several approaches can be taken. One way is to collect transcripts from various sources such as social media conversations, customer service interactions, academic discussions, and everyday conversations. This will help capture a wider range of language styles, topics, and participant backgrounds. Additionally, incorporating dialogues from different cultural contexts and languages can further enhance the diversity of the dataset. Collaborating with experts in sociolinguistics and discourse analysis can provide valuable insights into the types of dialogues that should be included to ensure a comprehensive representation of conversational patterns.

What other contextual factors, beyond the immediate dialog, could influence the perception of paraphrases in conversations?

Beyond the immediate dialog, several contextual factors can influence the perception of paraphrases in conversations. These factors include the relationship between the speakers, the cultural background of the participants, the setting in which the conversation takes place, and the overall communicative goals of the interaction. The emotional tone, non-verbal cues, and shared knowledge between the speakers can also impact how paraphrases are perceived. Additionally, the power dynamics, social status, and individual characteristics of the speakers can play a role in shaping the interpretation of paraphrases. Understanding these broader contextual factors is essential for accurately detecting and interpreting paraphrases in conversations.

How can the models be further improved to better capture the nuanced, context-dependent nature of paraphrases in natural dialog?

To better capture the nuanced, context-dependent nature of paraphrases in natural dialog, several strategies can be implemented. One approach is to incorporate multi-task learning techniques that consider not only the semantic equivalence of the text but also the contextual cues and speaker intentions. Fine-tuning models on a more diverse and extensive dataset that includes a wide range of conversational styles and topics can also enhance the model's ability to detect context-dependent paraphrases accurately. Additionally, leveraging pre-trained language models with larger contextual understanding and incorporating attention mechanisms that focus on specific parts of the conversation can improve the model's performance in capturing the subtle nuances of paraphrases in natural dialog. Regularly updating the model with new data and continuously evaluating its performance on real-world dialogues can further refine its ability to handle context-dependent paraphrases effectively.