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Found 31 papers, 12 papers with code
Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging
The concept of Dialogue Act (DA) is universal across different task-oriented dialogue domains - the act of {``}request{''} carries the same speaker intention whether it is for restaurant reservation or flight booking.
Few-shot Unified Question Answering: Tuning Models or Prompts?
Question-answering (QA) tasks often investigate specific question types, knowledge domains, or reasoning skills, leading to specialized models catering to specific categories of QA tasks.
Answering Complex Questions over Text by Hybrid Question Parsing and Execution
It comprises two central pillars: (1) We parse the question of varying complexity into an intermediate representation, named H-expression, which is composed of simple questions as the primitives and symbolic operations representing the relationships among them; (2) To execute the resulting H-expressions, we design a hybrid executor, which integrates the deterministic rules to translate the symbolic operations with a drop-in neural reader network to answer each decomposed simple question.
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
We evaluate our model's cross-lingual generalization capabilities on two conversation tasks: slot-filling and intent classification.
AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation
Dense retrievers have made significant strides in text retrieval and open-domain question answering, even though most achievements were made possible only with large amounts of human supervision.
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database
Parsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB).
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets.
Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors
We compare performance of state-of-the-art factuality metrics, including recent ChatGPT-based metrics, on this stratified benchmark and show that their performance varies significantly across different types of summarization models.
Modeling Multi-hop Question Answering as Single Sequence Prediction
Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA.
Converse: A Tree-Based Modular Task-Oriented Dialogue System
At the core of the struggle is the need to script every single turn of interactions between the bot and the human user.
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Among several interesting findings, it is important to highlight that (1) the generative readers perform better in long context QA, (2) the extractive readers perform better in short context while also showing better out-of-domain generalization, and (3) the encoder of encoder-decoder PrLMs (e. g., T5) turns out to be a strong extractive reader and outperforms the standard choice of encoder-only PrLMs (e. g., RoBERTa).
Dense Hierarchical Retrieval for Open-Domain Question Answering
In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.
RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability.
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data.
Stage-wise Fine-tuning for Graph-to-Text Generation
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders.
Ranked #3 on
Data-to-Text Generation
on WebNLG
(using extra training data)
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.
Ranked #2 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Unsupervised Paraphrasing with Pretrained Language Models
To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step.
A Simple Language Model for Task-Oriented Dialogue
Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.
Ranked #4 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.1
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data.
DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks
Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation.
Learning Question-Guided Video Representation for Multi-Turn Video Question Answering
Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans.
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization
Maximum-likelihood estimation (MLE) is one of the most widely used approaches for training structured prediction models for text-generation based natural language processing applications.
What It Takes to Achieve 100\% Condition Accuracy on WikiSQL
The SQL queries in WikiSQL are simple: Each involves one relation and does not have any join operation.
DialSQL: Dialogue Based Structured Query Generation
The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries.
Recovering Question Answering Errors via Query Revision
The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes.
Global Relation Embedding for Relation Extraction
We study the problem of textual relation embedding with distant supervision.
