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Found 16 papers, 4 papers with code
User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue
One of the major impediments to the development of new task-oriented dialogue (TOD) systems is the need for human evaluation at multiple stages and iterations of the development process.
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Intent classification (IC) plays an important role in task-oriented dialogue systems.
Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11
With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Label Semantic Aware Pre-training for Few-shot Text Classification
Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces.
Tanbih: Get To Know What You Are Reading
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
Adversarial Domain Adaptation for Duplicate Question Detection
We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions.
A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines
Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution.
Cross-Language Question Re-Ranking
We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings.
Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking
Community question answering (cQA) websites are focused on users who query questions onto an online forum, expecting for other users to provide them answers or suggestions.
Neural Attention for Learning to Rank Questions in Community Question Answering
In real-world data, e. g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms.
An Interactive System for Exploring Community Question Answering Forums
We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums.
Addressing Community Question Answering in English and Arabic
In the case of the Arabic question re-ranking task, for the first time we applied tree kernels on syntactic trees of Arabic sentences.
