30 research outputs found
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue
and it has a significant impact both on usability and perceived quality. Most
NLG systems in common use employ rules and heuristics and tend to generate
rigid and stylised responses without the natural variation of human language.
They are also not easily scaled to systems covering multiple domains and
languages. This paper presents a statistical language generator based on a
semantically controlled Long Short-term Memory (LSTM) structure. The LSTM
generator can learn from unaligned data by jointly optimising sentence planning
and surface realisation using a simple cross entropy training criterion, and
language variation can be easily achieved by sampling from output candidates.
With fewer heuristics, an objective evaluation in two differing test domains
showed the proposed method improved performance compared to previous methods.
Human judges scored the LSTM system higher on informativeness and naturalness
and overall preferred it to the other systems.Comment: To be appear in EMNLP 201
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Statistical spoken dialogue systems have the attractive property of being
able to be optimised from data via interactions with real users. However in the
reinforcement learning paradigm the dialogue manager (agent) often requires
significant time to explore the state-action space to learn to behave in a
desirable manner. This is a critical issue when the system is trained on-line
with real users where learning costs are expensive. Reward shaping is one
promising technique for addressing these concerns. Here we examine three
recurrent neural network (RNN) approaches for providing reward shaping
information in addition to the primary (task-orientated) environmental
feedback. These RNNs are trained on returns from dialogues generated by a
simulated user and attempt to diffuse the overall evaluation of the dialogue
back down to the turn level to guide the agent towards good behaviour faster.
In both simulated and real user scenarios these RNNs are shown to increase
policy learning speed. Importantly, they do not require prior knowledge of the
user's goal.Comment: Accepted for publication in SigDial 201
Sadržaj polifenola u grožđu različitih klonova sorte Cabernet franc selekcionisanih u Srbiji
Polyphenols are a large group of structurally diverse compounds widely represented in plants. To a large extent contribute to the nutritional and organoleptic characteristics of fruits and vegetables. The aim of the study was to determine the contents of a selected flavan-3-ols in grape of Cabernet Franc clones (No. 02, 010 and 012) obtained in the last phase perennial clonal selection of the variety in the Republic of Serbia. The enriched content of catechin, epicatechin, epigallocatechin, gallocatechin gallate and catechin gallate, compared to the standard and the other two clones specifically allocated No. 010.Polifenoli su velika grupa strukturno različitih jednjenja široko zastupljenih u biljkama. U velikoj meri doprinose hranjivosti i organoleptičkim osobinama voća i povrća. Cilj rada bio je određivanje sadržaja odabranih flavan-3-ola u grožđu Cabernet Franc klonova (No.02, 010 i 012) dobijenih u poslednjoj fazi višegodišnje klonske selekcije sorte u Republici Srbiji. Po obogaćenom sadržaju katehina, epigalokatehina, epikatehina, epikatehin galata, i katehin galata u grožđu u odnosu na standard i druga dva klona posebno se izdvaja klon No. 010