Search Results for author:
Found 16 papers, 14 papers with code
GL-CLeF: A Global–Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
Specifically, we employ contrastive learning, leveraging bilingual dictionaries to construct multilingual views of the same utterance, then encourage their representations to be more similar than negative example pairs, which achieves to explicitly align representations of similar sentences across languages.
CGIM: A Cycle Guided Interactive Learning Model for Consistency Identification in Task-oriented Dialogue
Consistency identification in task-oriented dialog (CI-ToD) usually consists of three subtasks, aiming to identify inconsistency between current system response and current user response, dialog history and the corresponding knowledge base.
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity.
OpenAgents: An Open Platform for Language Agents in the Wild
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
Lemur: Harmonizing Natural Language and Code for Language Agents
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.
Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback.
Binding Language Models in Symbolic Languages
We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e. g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations.
Ranked #4 on
Table-based Fact Verification
on TabFact
NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries
We develop NL2INTERFACE to explore the potential of generating usable interactive multi-visualization interfaces from natural language queries.
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
We present Global--Local Contrastive Learning Framework (GL-CLeF) to address this shortcoming.
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.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System
Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
Ranked #1 on
Semantic Frame Parsing
on MixATIS
(Overall Accuracy metric)
A Survey on Spoken Language Understanding: Recent Advances and New Frontiers
Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system.
