I'd point out one more paper that might be relevant to your problem: https://www.aclweb.org/anthology/P17-1141/. The main reason is because they have released code (available in https://github.com/chrishokamp/constrained_decoding), which may make your life a bit easier in experimenting with different ideas.
Best, Iacer Calixto. *Iacer Calixto* Postgraduate Research Fellow, *Marie Skłodowska-Curie Global Fellow* - Courant Institute of Mathematical Sciences, New York University (NYU) - Institute for Logic, Language and Computation, The University of Amsterdam (UvA) - e-mail: iacer.calixto at nyu.edu, iacer.calixto at uva.nl - website: iacercalixto.github.io
On Wed, Mar 11, 2020 at 11:05 AM Jason Eisner <jason at cs.jhu.edu> wrote:
> I don't think you need to change the model p(x) itself.
> You are really asking about algorithms for choosing strings from the
> conditional distribution p(x | x contains "university"), which is fully
> determined by p(x).
> (Either you want to sample random strings from that distribution, or try
> to choose the best such string.)
> Here is a recent paper with some references:
> Here is an example of an earlier paper that uses context-free generation
> instead of neural generation:
> You may also be interested in related settings such as conditioning on x
> having a particular meaning, being in a particular formal language, or
> following the constraints of a poetic form:
> Jason Eisner
> On Wed, Mar 11, 2020 at 10:35 AM saedeh tahery <saedeh.tahery at gmail.com>
>> Dear all,
>> Recently, I've found generative models such as SeqGan that can produce
>> discrete sequences (i.e., text). I know they can generate some real-like
>> texts. However, is there any generative model that can produce a sequence
>> containing some specific tokens? E.g., a model generates sentences that
>> include the particular word "university".
>> Any guidance would be appreciated.
>> Best Regards,
>> Sa'edeh Tahery
>> Ph.D. Student, Artificial Intelligence
>> K. N. Toosi University of Technology
>> Tehran, Iran
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