[Corpora-List] Semantic parsing for the robot commands corpus by integrating spatial knowledge

[Chris Brew]

It'll be much easier to do the feedback from dialog context and real-world
situation if you use a parser that is designed, from the outset, to
integrate information from multiple levels of representation.

One such is XLE http://www2.parc.com/isl/groups/nltt/xle/

To do this with XLE, you would need to have, or write, a grammar that works
for your domain of interest. That may be daunting, but actually, I think
the representational challenges in retrofitting semantic and pragmatic
sensitivity onto present-day statistical parsers are just as daunting,
albeit in a different way.Goldberg's parsers are really nice (cf. Honnibal,
Goldberg, Johnson, 2013 at CONLL2013 for the latest and greatest), but the
problems that they solve are only a subset of what you need to solve your
problem.

Do inform the list what approach you decide to take. A good general
solution would be wonderful to have.

Chris




On Fri, Sep 6, 2013 at 11:55 PM, Kais Dukes <sckd at leeds.ac.uk> wrote:

> Dear Corpora List,
>
> Firstly, some good news - The Robot Commands Annotation Corpus (
> http://www.trainrobots.com) is growing quite well, and we're getting
> close to 100,000 words thanks to everyone who's playing. However, before
> releasing the data, I'm keen to do some annotation, starting with automatic
> parsing.
>
> A couple of things have surprised me with this new experiment for
> crowdsourcing robot commands online. Firstly, a lot of commands can be long
> and linguistically rich. Forgetting those for a moment, I think even
> parsing simple commands to start with can be tricky. This is because many
> commands that players have been typing in are impossible to parse correctly
> without spatial context. The following is a real example that a player
> typed in which only makes sense in the context of the images in the game:
>
> "Place the red block on the yellow block on the blue block in the top left
> corner."
>
> This could mean any of these possible moves:
>
> 1. Move red block (on yellow block on blue block), and put this in top
> left corner.
> 2. Move red block (on yellow block), and put this on blue block (in top
> left corner).
> 3. Move red block, and put this on yellow block (on blue block in top left
> corner).
>
> I've been considering several ways to do semantic parsing for the corpus,
> e.g. CCG parsing, using Stanford dependency parser, etc. My concern with
> these, as far as I understand, is that they would require a pipeline
> approach. Making understanding the above sentence quite problematic.
> However, what it really like to do is to use a parsing algorithm that
> allows me to incorporate spatial knowledge. I would rather not do a
> brute-force search of many possible parse trees output by a parser, but
> would rather do something at parse-time.
>
> After some consideration I’m thinking of writing a custom statistical
> parser based on Goldberg and Elhadad's non-directional dependency algorithm
> (http://www.aclweb.org/anthology-new/N/N10/N10-1115.pdf). My intuition
> here is that a parser using this sort of algorithm will allow me to tune
> the scoring function to including spatial knowledge and make it easier to
> perform correct long-distance PP-attachment at parse-time.
>
> In a nutshell, I’m looking for any suggestions on how to do joint parsing
> / spatial disambiguation. Ideally not a pipeline approach, but something
> integrated. Surely there must be a good way to use spatial knowledge while
> parsing? Could my idea of modifying the non-directional dependency parser
> work? The parser could then leverage a relational knowledge base for each
> scene, e.g. if the parser knew that the red block is on the yellow block,
> it could produce the correct parse tree for the above example. Hopefully
> this same approach would reduce error-propagation when I come to the larger
> more complex sentences in the corpus.
>
> Feedback or thoughts would be most welcome.
>
> Regards,
> -- Kais
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