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Found 23 papers, 9 papers with code
User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue
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 process.
DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems
Dialogue act annotations are important to improve response generation quality in task-oriented dialogue systems.
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Intent classification (IC) plays an important role in task-oriented dialogue systems.
Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11
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 induction of slots and dialogue states.
Conversation Style Transfer using Few-Shot Learning
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e. g., formality).
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Federated Semi-Supervised Learning with Prototypical Networks
Compared to a FSSL approach based on weight sharing, the prototype-based inter-client knowledge sharing significantly reduces both communication and computation costs, enabling more frequent knowledge sharing between more clients for better accuracy.
Reward Optimization for Neural Machine Translation with Learned Metrics
Results show that the reward optimization with BLEURT is able to increase the metric scores by a large margin, in contrast to limited gain when training with smoothed BLEU.
GraphPlan: Story Generation by Planning with Event Graph
In this paper, we focus on planning a sequence of events assisted by event graphs, and use the events to guide the generator.
Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation
Given a continuous latent variable model for machine translation (Shu et al., 2020), we train an inference network to approximate the gradient of the marginal log probability of the target sentence, using only the latent variable as input.
Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior
By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1. 0 BLEU point on WMT'14 En-De task with 6. 8x speedup.
Generating Diverse Translations with Sentence Codes
In this work, we attempt to obtain diverse translations by using sentence codes to condition the sentence generation.
Enabling Real-time Neural IME with Incremental Vocabulary Selection
Input method editor (IME) converts sequential alphabet key inputs to words in a target language.
Real-time Neural-based Input Method
The input method is an essential service on every mobile and desktop devices that provides text suggestions.
Discrete Structural Planning for Generating Diverse Translations
Planning is important for humans when producing complex languages, which is a missing part in current language generation models.
Discrete Structural Planning for Neural Machine Translation
Structural planning is important for producing long sentences, which is a missing part in current language generation models.
Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation
However, as the algorithm produces hypotheses in a monotonic left-to-right order, a hypothesis can not be revisited once it is discarded.
Compressing Word Embeddings via Deep Compositional Code Learning
For each word, the composition of basis vectors is determined by a hash code.
Ranked #10 on
Machine Translation
on IWSLT2015 German-English
Single-Queue Decoding for Neural Machine Translation
Neural machine translation models rely on the beam search algorithm for decoding.
Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation
For extended periods of time, sequence generation models rely on beam search algorithm to generate output sequence.
An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation
Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models.
Residual Stacking of RNNs for Neural Machine Translation
To enhance Neural Machine Translation models, several obvious ways such as enlarging the hidden size of recurrent layers and stacking multiple layers of RNN can be considered.
Generating Video Description using Sequence-to-sequence Model with Temporal Attention
Automatic video description generation has recently been getting attention after rapid advancement in image caption generation.
