Search Results for author:
Found 5 papers, 1 papers with code
Relational Surrogate Loss Learning
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.
Mental Health Assessment for the Chatbots
Previous researches on dialogue system assessment usually focus on the quality evaluation (e. g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics.
Modeling Coverage for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously.
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs.
Ranked #6 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produces an unbalanced translation.
