Incremental LSTM-based dialog state tracker

Žilka, Lukáš; Jurčíček, Filip

doi:10.1109/asru.2015.7404864

Cited by 85 publications

(50 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such systems are prone to error accumulation from a separate SLU module, in absence of necessary dialog context required to interpret the user utterance. Thus, later work on DST moved away from separate SLU modules and inferred the dialog state directly from user utterance and dialog history (Henderson et al, 2014b,c;Zilka and Jurcicek, 2015). These models depend on delexicalization, using generic tags to replace specific slot types and values, and handcrafted semantic dictionaries.…”

Section: Related Workmentioning

confidence: 99%

Improving Dialogue State Tracking by Discerning the Relevant Context

Sharma

Choubey

Huang

2019

Proceedings of the 2019 Conference of the North

View full text Add to dashboard Cite

A typical conversation comprises of multiple turns between participants where they go back-and-forth between different topics. At each user turn, dialogue state tracking (DST) aims to estimate user's goal by processing the current utterance. However, in many turns, users implicitly refer to the previous goal, necessitating the use of relevant dialogue history. Nonetheless, distinguishing relevant history is challenging and a popular method of using dialogue recency for that is inefficient. We, therefore, propose a novel framework for DST that identifies relevant historical context by referring to the past utterances where a particular slot-value changes and uses that together with weighted system utterance to identify the relevant context. Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance. Empirical analyses show that our method improves joint goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Mul-tiWoZ 2.0 restaurant domain datasets respectively over the previous state-of-the-art GLAD model.

show abstract

Section: Related Workmentioning

confidence: 99%

Improving Dialogue State Tracking by Discerning the Relevant Context

Sharma

Choubey

Huang

2019

Proceedings of the 2019 Conference of the North

View full text Add to dashboard Cite

show abstract

“…Dialogue state tracking Dialogue state tracking (DST) focuses on tracking conversational states as well. Traditional DST models rely on handcrafted semantic delexicalization to achieve generalization (Henderson et al, 2014;Zilka and Jurcícek, 2015;Mrksic et al, 2015). Mrksic et al (2017) utilize representation learning for states rather than using hand-crafted features.…”

Section: Error Analysismentioning

confidence: 99%

Improving Long Distance Slot Carryover in Spoken Dialogue Systems

Chen¹,

Naik²,

He³

et al. 2019

Proceedings of the First Workshop on NLP for Conversational AI

View full text Add to dashboard Cite

Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn. Previous work on the slot carryover task used models that made independent decisions for each slot. A close analysis of the results show that this approach results in poor performance over longer context dialogues. In this paper, we propose to jointly model the slots. We propose two neural network architectures, one based on pointer networks that incorporate slot ordering information, and the other based on transformer networks that uses self attention mechanism to model the slot interdependencies. Our experiments on an internal dialogue benchmark dataset and on the public DSTC2 dataset demonstrate that our proposed models are able to resolve longer distance slot references and are able to achieve competitive performance. x 1 x 2 x 3 x 4 I i c ✓ ✗ ✗ ✓ Decoder Intent Encoder Dialogue Encoder (bi-LSTM) Serialized Dialogue Token Embeddings Contextual Embeddings h A 1 < l a t e x i t s h a 1 _ b a s e 6 4 = " h q y x w m I P y D k y 4 H n J D 7 F b g 9 t Z G Q o = " > A A A C A n i c b V D L T g I x F L 2 D L 8 Q X 6 t J N I z F x R W a M i S 5 R N y 4 x c Y A E R t I p H W h o O 5 O 2 Y y S E n X u 3 + g v u j F t / x D / w M + z A L A Q 8 S Z O T c + 6 r J 0 w 4 0 8 Z 1 v 5 3 C y u r a + k Z x s 7 S 1 v b O 7 V 9 4 / a O g 4 V Y T 6 J O a x a o V Y U 8 4 k 9 Q 0 z n L Y S R b E I O W 2 G w 5 v M b z 5 S p V k s 7 8 0 o o Y H A f c k i R r C x k j / o e g 9 X 3 X L F r b p T o G X i 5 a Q C O e r d 8 k + n F 5 N U U G k I x 1 q 3 P T c x w R g r w w i n k 1 I n 1 T T B Z I j 7 t G 2 p x I L q Y D w 9 d o J O r N J D U a z s k w Z N 1 b 8 d Y y y 0 H o a 1 _ b a s e 6 4 = " h q y x w m I P y D k y 4 H n Je g 9 X 3 X L F r b p T o G X i 5 a Q C O e r d 8 k + n F 5 N U U G k I x 1 q 3 P T c x w R g r w w i n k 1 I n 1 T T B Z I j 7 t G 2 p x I L q Y D w 9 d o J O r N J D U a z s k w Z N 1 b 8 d Y y y 0 H o a 1 _ b a s e 6 4 = " h q y x w m I P y D k y 4 H n J D 7 F b g 9 t Z G Q o = " > A A A C A n i c b V D L T g I x F L 2 D L 8 Q X 6 t J N I z F x R W a M i S 5 R N y 4 x c Y A E R t I p H W h o O 5 O 2 Y y S E n X u 3 + g v u j F t / x D / w M + z A L A Q 8 S Z O T c + 6 r J 0 w 4 0 8 Z 1 v 5 3 C y u r a + k Z x s 7 S 1 v b O 7 V 9 4 / a O g 4 V Y T 6 J O a x a o V Y U 8 4 k 9 Q 0 z n L Y S R b E I O W 2 G w 5 v M b z 5 S p V k s 7 8 0 o o Y H A f c k i R r C x k j / o e g 9 X 3 X L F r b p T o G X i 5 a Q C O e r d 8 k + n F 5 N U U G k I x 1 q 3 P T c x w R g r w w i n k 1 I n 1 T T B Z I j 7 t G 2 p x I L q Y D w 9 d o J O r N J D U a z s k w Z N 1 b 8 d Y y y 0 H o a 1 _ b a s e 6 4 = " 3 K N N 2 9 b r 2 o 9 T 8 I 1 E R 0 O N a m 5 p 2 0 I = " > A A A C A n i c b V D L S g M x F L 1 T X 3 W s W t d u g k V w V W b c 6 F J w 4 7 K C f U B b S i a T a U O T z J D c E c v Q H 3 A n f o w 7 8 T f 8 F j d m 2 i 5 s 6 4 U L h 3 M S z r k n y q S w G A T...

show abstract

“…. , vn i } takes one of the ni values [9]. The goal of a dialog state tracker consists of mapping a sequence of words w1, .…”

Section: Incremental Dialog State Trackingmentioning

confidence: 99%

“…We build our incremental Dialog State Tracker (iDST) by taking as reference the LecTrack model [9]. The iDST was used as a starting block for the implementation of our incremental Turn-Taking Decider (iTTD), which in turn is responsible for identifying the best point that balances the accuracy of the iDST and the early turn-taking moment using the least amount of tokens possible.…”

Section: Introductionmentioning

confidence: 99%

An Incremental Turn-Taking Model for Task-Oriented Dialog Systems

et al. 2019

View full text Add to dashboard Cite

In a human-machine dialog scenario, deciding the appropriate time for the machine to take the turn is an open research problem. In contrast, humans engaged in conversations are able to timely decide when to interrupt the speaker for competitive or non-competitive reasons. In state-of-the-art turn-by-turn dialog systems the decision on the next dialog action is taken at the end of the utterance. In this paper, we propose a token-by-token prediction of the dialog state from incremental transcriptions of the user utterance. To identify the point of maximal understanding in an ongoing utterance, we a) implement an incremental Dialog State Tracker which is updated on a token basis (iDST) b) re-label the Dialog State Tracking Challenge 2 (DSTC2) dataset and c) adapt it to the incremental turn-taking experimental scenario. The re-labeling consists of assigning a binary value to each token in the user utterance that allows to identify the appropriate point for taking the turn. Finally, we implement an incremental Turn Taking Decider (iTTD) that is trained on these new labels for the turn-taking decision. We show that the proposed model can achieve a better performance compared to a deterministic handcrafted turn-taking algorithm.

show abstract

Incremental LSTM-based dialog state tracker

Cited by 85 publications

References 15 publications

Improving Dialogue State Tracking by Discerning the Relevant Context

Improving Dialogue State Tracking by Discerning the Relevant Context

Improving Long Distance Slot Carryover in Spoken Dialogue Systems

An Incremental Turn-Taking Model for Task-Oriented Dialog Systems

Contact Info

Product

Resources

About