T2S: An Encoder-Decoder Model for Topic-Based Natural Language Generation

Ou, Wenjie; Chen, Chaotao; Ren, Jiangtao

doi:10.1007/978-3-319-91947-8_15

Cited by 2 publications

(7 citation statements)

References 19 publications

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“…Many works have been proposed to utilize the static topic information to improve the generation performance. Chen et al (2016) and Ou et al (2018) propose to represent the topic for each sentence as a learnable vector. The topic is predicted by the input sentence and is used to enhance the generating phase.…”

Section: Company Topicmentioning

confidence: 99%

“…Xing et al (2017) and Zhang et al (2016) detect the topic representation by applying a pre-trained LDA model on the input sequence. Moreover, Choudhary et al (2017) and Ou et al (2018) predict the topic representation directly from the input sequence using Recurrent Neural Networks (RNN). All the above methods make an assumption that during generation the topic does not change so as to make the problem tractable, which scarifies the advantage of modeling the dynamic nature of topic information.…”

Section: Company Topicmentioning

confidence: 99%

“…Mikolov and Zweig (2012) as well as Liu et al (2015) propose to use the topic information as extra features to enhance the performance of the language model and word embedding. Chen et al (2016) and Ou et al (2018) use the same idea to utilize the topic feature to enhance the generation of the text. However, all these methods assume that the topic information is known in advance.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Choudhary et al (2017) propose to train a classifier to predict the topic of the source sentence and use it to help generate the dialog response. Ou et al (2018) also propose to predict the topic vector directly from the input sequence. Dathathri et al (2020) propose to use the topic information as a reward function.…”

Section: Related Workmentioning

confidence: 99%

“…TopicFeature follows the model proposed by (Chen et al, 2016;Ou et al, 2018), this model learns a vector representation for each domain tag rather than just adding it as an additional word. For each domain, we denote it as a one-hot vector and the one-hot vector is fed into a feedforward layer to get the topic membership vector, which is then used as the extra feature to predict the decoder output.…”

Section: Bleumentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Topic Tracker for KB-to-Text Generation

Lam

Jameel

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.

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Section: Company Topicmentioning

confidence: 99%

Section: Company Topicmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Bleumentioning

confidence: 99%

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Dynamic Topic Tracker for KB-to-Text Generation

Lam

Jameel

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building

et al. 2020

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Smart WiFi thermostats have moved well beyond the function they were originally designed for; namely, controlling heating and cooling comfort in buildings. They are now also learning from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart WiFi thermostat data in residences to develop dynamic predictive models for room temperature and cooling/heating demand. These models can then be used to estimate the energy savings from new thermostat temperature schedules and estimate peak load reduction achievable from maintaining a residence in a minimum thermal comfort condition. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM), and Encoder-Decoder LSTM dynamic models are explored. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding and a MAE error of 0.5 °C, equal to the resolution error of the measured temperature. Additionally, the models developed are shown to be highly accurate in predicting savings from aggressive thermostat set point schedules, yielding deep reduction of up to 14.3% for heating and cooling, as well as significant energy reduction from curtailed thermal comfort in response to a high demand event.

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T2S: An Encoder-Decoder Model for Topic-Based Natural Language Generation

Cited by 2 publications

References 19 publications

Dynamic Topic Tracker for KB-to-Text Generation

Dynamic Topic Tracker for KB-to-Text Generation

Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building

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