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Backward-Forward Sequence Generative Network for Multiple Lexical Constraints


Seemab Latif , Sarmad Bashir, Mir Muntasar Ali Agha , Rabia Latif

Publication Type:

Conference/Workshop Paper


16th International Conference on Artificial Intelligence Applications and Innovations






Advancements in Long Short Term Memory (LSTM) Networks have shown remarkable success in various Natural Language Generation (NLG) tasks. However, generating sequence from pre-specified lexical constraints is a new, challenging and less researched area in NLG. Lexical constraints take the form of words in the language model’s output to create fluent and meaningful sequences. Furthermore, most of the previous approaches cater this problem by allowing the inclusion of pre-specified lexical constraints during the decoding process, which increases the decoding complexity exponentially or linearly with the number of constraints. Moreover, some of the previous approaches can only deal with single constraint. In this paper, we propose a novel neural probabilistic architecture based on backward-forward language model and word embedding substitution method that can cater multiple lexical constraints for generating quality sequences. Experiments shows that our proposed architecture outperforms previous methods in terms of intrinsic evaluation.


author = {Seemab Latif and Sarmad Bashir and Mir Muntasar Ali Agha and Rabia Latif},
title = {Backward-Forward Sequence Generative Network for Multiple Lexical Constraints},
pages = {39--50},
month = {May},
year = {2020},
booktitle = {16th International Conference on Artificial Intelligence Applications and Innovations},
publisher = {Springer},
url = {}