Thought vector is fed into decoder on each decoding step. Decoder can be conditioned on any categorical label, for example, emotion label or persona id. Word embedding layer: May be initialized using w2v model trained on your corpus.
Consider running python tools/train_w2v.py to build w2v embedding from the training corpus. Warning: this script overwrites the original w2v weights that are stored in data/w2v_models. You should only run this script in case your corpus is large enough to contain all the words that you ...
candidate pair是当前循环考察的slot-value对,由slot和value的embedding组成,即二元组 。 最后的输出为一个二分类的标签,表示当前的candidate pair是否代表了当前时刻的dialog state。可以看出,在这里只需要定义domain ontology,不需要用delexicalisation的方法手工提取特征或者定义semantic dictionary。因此这种方法很适合处理...
Thought vector is fed into decoder on each decoding step. Decoder can be conditioned on any categorical label, for example, emotion label or persona id. Word embedding layer: May be initialized using w2v model trained on your corpus.
starspace: a simple supervised embedding approach which is a strong baseline based on this paper. tfidf_retriever a simple retrieval based model, also useful as a first step for retrieving information as input to another model. ir_baseline: simple information retrieval baseline that scores candidate...
First layer of the utterance-level encoder is always bidirectional. Thought vector is fed into decoder on each decoding step. Decoder can be conditioned on any string label. For example: emotion label or id of a person talking. Word embedding layer: May be initialized using w2v model ...
for each of the candidate values for the slot, wherein generating the score for each of the candidate values is based on processing, using a trained scoring model: the system utterance representation, the user utterance representation, the system candidate value features for the candidate value, ...
language. The language model may be, for example, a unigram model, an n-gram model, a neural-network model, or any other model. The present invention is not limited to any particular type of encoder/decoder or translation/language models....
The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,”“an,” ...
(c) judging whether or not an inquiry of gaming history information of the player owning a portable memory is requested in the conversation sentence having the data analyzed by the conversation engine; and (d) upon the inquiry of the gaming history information being requested in the conversation...