Recent studies in
dialogue state tracking (DST) leverage historical information to determine states which are generally
represented as slot-value pairs. However, most of them have limitations to efficiently exploit relevant
context due to the lack of a powerful mechanism for modeling interactions between the slot and the
dialogue history. Besides, existing methods usually ignore the slot imbalance problem and treat all slots
indiscriminately, which limits the learning of hard slots and eventually hurts overall performance. In
this paper, we propose to enhance the DST through employing a contextual hierarchical attention network to
not only discern relevant information at both word level and turn level but also learn contextual
representations. We further propose an adaptive objective to alleviate the slot imbalance problem by
dynamically adjust weights of different slots during training. Experimental results show that our approach
reaches 52.68% and 58.55% joint accuracy on MultiWOZ 2.0 and MultiWOZ 2.1 datasets respectively and
achieves new state-of-the-art performance with considerable improvements (+1.24% and +5.98%).
Yong Shan, Yang Feng, Jinchao Zhang, Fandong Meng and Wen Zhang. Improving Bidirectional
Decoding with Dynamic Target Semantics for Neural Machine Translation. In CoRR.
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. Recent
works attempt to utilize the right-to-left (R2L) decoder in bidirectional decoding to alleviate this
In this paper, we propose a novel Dynamic Interaction Module (DIM) to dynamically exploit target
semantics from R2L translation for enhancing the L2R translation quality.
Different from other bidirectional decoding approaches, DIM firstly extracts helpful target
information through addressing and reading operations, then updates target semantics for tracking the
Additionally, we further introduce an agreement regularization term into the training objective to
narrow the gap between L2R and R2L translations.
Experimental results on NIST Chinese-English and WMT'16 English-Romanian translation tasks show that
our system achieves significant improvements over baseline systems, which also reaches comparable
results compared to the state-of-the-art Transformer model with much fewer parameters of it.