Search Results for author:
Found 14 papers, 4 papers with code
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
To study this, we propose the problem of faithful planning in TODs that needs to resolve user intents by following predefined flows and preserving API dependencies.
Eliciting Better Multilingual Structured Reasoning from LLMs through Code
xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks.
DeAL: Decoding-time Alignment for Large Language Models
Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF).
Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data.
DFEE: Interactive DataFlow Execution and Evaluation Kit
DataFlow has been emerging as a new paradigm for building task-oriented chatbots due to its expressive semantic representations of the dialogue tasks.
Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems
Practically, some combinations of slot values can be invalid according to external knowledge.
A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection
An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i. e., Answer Sentence Selection (A2S).
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question.
Injecting Relational Structural Representation in Neural Networks for Question Similarity
Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e. g., question similarity, is still an open problem.
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model
Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking.
Effective shared representations with Multitask Learning for Community Question Answering
An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineering features.
Multitask Learning with Deep Neural Networks for Community Question Answering
In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i. e., question-comment similarity, question-question similarity and new question-comment similarity.
