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20162023
most citedUser Simulation with Large Language Models for Evaluating Task-Oriented Dialogue

4 citations · 10 across the 9 of their papers we have counts for

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9 papers · 1 filter

cs.CL2023★ 4 cited

User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue

Sam Davidson, Salvatore Romeo, Raphael Shu +4

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 pr…

cs.CL2023

Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification

Mujeen Sung, James Gung, Elman Mansimov +5

Intent classification (IC) plays an important role in task-oriented dialogue systems. However, IC models often generalize poorly when training without sufficient annotated examples…

cs.CL2023★ 3 cited

Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11

James Gung, Raphael Shu, Emily Moeng +6

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 i…

cs.CL2022★ 1 cited

Dialog2API: Task-Oriented Dialogue with API Description and Example Programs

Raphael Shu, Elman Mansimov, Tamer Alkhouli +6

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing…

cs.CL2022★ 1 cited

Label Semantic Aware Pre-training for Few-shot Text Classification

Aaron Mueller, Jason Krone, Salvatore Romeo +4

In text classification tasks, useful information is encoded in the label names. Label semantic aware systems have leveraged this information for improved text classification perfor…

cs.CL2021

Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings

Sawsan Alqahtani, Garima Lalwani, Yi Zhang +2

Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target emb…

cs.CL2019★ 1 cited

Tanbih: Get To Know What You Are Reading

Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeño +10

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story. Our system displays news grouped into events and ge…

cs.CL2017

Cross-Language Question Re-Ranking

Giovanni Da San Martino, Salvatore Romeo, Alberto Barron-Cedeno +4

We study how to find relevant questions in community forums when the language of the new questions is different from that of the existing questions in the forum. In particular, we…

cs.CL2016

Addressing Community Question Answering in English and Arabic

Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo +5

This paper studies the impact of different types of features applied to learning to re-rank questions in community Question Answering. We tested our models on two datasets released…