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Found 10 papers, 5 papers with code
OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking
Large language models (LLMs) have revolutionized the landscape of Natural Language Processing systems, but are computationally expensive.
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons.
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages.
In-Context Learning for Few-Shot Dialogue State Tracking
In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates.
DOCmT5: Document-Level Pretraining of Multilingual Language Models
In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large scale parallel documents.
Ranked #1 on
Document Translation
on WMT 2020
Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots.
Ranked #1 on
Dialogue State Tracking
on MULTIWOZ 2.1
(MultiWOZ (Joint Goal Acc) metric)
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains.
Ranked #1 on
Text-To-SQL
on KaggleDBQA
Cross-Lingual Transfer Learning for Question Answering
In this paper, we explore the problem of cross-lingual transfer learning for QA, where a source language task with plentiful annotations is utilized to improve the performance of a QA model on a target language task with limited available annotations.
Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation
Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents.
Ranked #3 on
Spoken Language Understanding
on Spoken-SQuAD
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
ODSQA: Open-domain Spoken Question Answering Dataset
Reading comprehension by machine has been widely studied, but machine comprehension of spoken content is still a less investigated problem.
