| Challenge: | Existing methods for intent classification rely on a single user input and do not interact with the user to reduce ambiguity and improve the final prediction. |
| Approach: | They propose a limited form of interaction to natural language intent classification . they add binary or multi-choice questions to the system to ask missing information . |
| Outcome: | The proposed method can be bootstrapped without interaction data and is scalable to two domains. |
Similar Papers
The Why and The How: A Survey on Natural Language Interaction in Visualization (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent research shows that different forms of natural language-based interaction prove suitable to support users in accomplishing various visualization tasks. |
| Approach: | They propose a taxonomy of visualization tasks and a classification system to illustrate the state-of-the-art of natural language-based interaction in visualization. |
| Outcome: | The proposed model can support annotations, recommendations, explanations, and documentation tasks. |
INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) absorb static data without the ability to question and refine knowledge. |
| Approach: | They propose a framework in which a “student” LLM engages a ‘teacher’ LLM through iterative inquiries to acquire knowledge across 1,347 contexts. |
| Outcome: | The proposed framework achieves up to 25% improvement in 1,347 contexts across a wide range of scenarios and LLM architectures, with ‘cold-start’ student models matching static learning baselines in as few as five dialogue turns. |
Going beyond research datasets: Novel intent discovery in the industry setting (2023.findings-eacl)
Copied to clipboard
| Challenge: | Novel intent discovery automates grouping of similar messages to identify previously unknown intents. |
| Approach: | They propose to use question-only data to improve the intent discovery pipeline . they propose to utilize conversational structure of real-life datasets for clustering . |
| Outcome: | The proposed method gives 33pp performance boost over state-of-the-art model for question only . it also gives 13pp performance increase over the naive baseline model . |
Integrating Question Classification and Deep Learning for improved Answer Selection (C18-1)
Copied to clipboard
| Challenge: | Question Answering (QA) is the task of automatically generating answers to questions posed in natural language. |
| Approach: | They propose a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer selection. |
| Outcome: | The proposed system outperforms the current state of the art in all variations except one . the proposed system improves QA by reducing the search space of potential answers . |
Designing, Evaluating, and Learning from Humans Interacting with NLP Models (2023.emnlp-tutorial)
Copied to clipboard
| Challenge: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
| Approach: | They will provide a systematic overview of key considerations and effective approaches for studying human-NLP model interactions. |
| Outcome: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing methods that focus on learning a ranking across the whole candidate space are lacking user or task-specific training data. |
| Approach: | They propose an interactive ranking approach that actively selects pairs of candidates, from which the user selects the best. |
| Outcome: | The proposed approach outperforms existing methods in community question answering and extractive multidocument summarization and is an effective reward function for reinforcement learning. |
Open-Domain Question Answering (2020.acl-tutorials)
Copied to clipboard
| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
| Approach: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA . |
| Outcome: | The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods . |
Generative Interfaces for Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are increasingly seen as assistants, copilots, and consultants . however, their linear request-response format often makes interactions inefficient in multi-turn tasks . |
| Approach: | They propose a paradigm in which large language models respond to user queries by generating user interfaces that enable more adaptive and interactive engagement. |
| Outcome: | The proposed paradigm outperforms traditional chat-based interfaces in many tasks and interaction patterns. |
Cross-lingual intent classification in a low resource industrial setting (D19-1)
Copied to clipboard
| Challenge: | Recent advances in multilingual text representations promise cross-lingual transfer for classifiers . but practical application calls for a combination of techniques for useful results . |
| Approach: | They investigate different approaches to multilingual intent classification in a low resource setting. |
| Outcome: | The proposed methods show promise, but practical application calls for a combination of techniques. |
Interactive Language Learning by Question Answering (D19-1)
Copied to clipboard
| Challenge: | Existing machine reading comprehension tasks lack interactive information-seeking component of comprehension. |
| Approach: | They propose a question-asking task that asks questions in a text-based environment . they propose QAit, which uses a game generator to build models that include deep reinforcement learning agents. |
| Outcome: | The proposed task poses questions about existence, location, and attributes of objects found in environment. |