Future directions for HPSG parsing

Chair: Olga Scribe: John

Initial notes

Task: [More HPSG] constituency … dependency [Less HPSG? But more useful?] Downstream (what is needed to make progress there?) for grammar correction? For gold standard annotation?

Efficiency: Packing: under subsumption; under generalization

Structure/type of algorithm:

Limitations:

  • What can and cannot be improved, in principle? What kind of gains are we talking about, in general?

Evaluation:

  • What do we compare against?

Formalism:

  • JRF
    • Relational constraints? :slightly_smiling_face:

    MRS

Discussion

Olga: Introduced the topic. She noted that the IWPT paper above elicited positive reactions when presented; the results are surprising since many researchers have given up on top-down parsing for HPSG.

John: Do different tasks have different requirements for parsing efficiency? Earlier today we heard about successes in producing semantic representations using DELPH-IN tools – for this task, is current HPSG parsing efficiency adequate? Specifically, is ACE fast enough to always produce a full forest that a grammarian can disambiguate?

Dan: For constructing treebanks, parsing speed is not an issue with ACE and the ERG - only around 1% of current English treebank sentences fail to get a packed forest in 300 secs CPU time and 16GB memory. ACE is able to produce up to 10^18 packed analyses. But with Alexandre’s mining corpus containing 50 or 60-word sentences, the failure rate is as high as 30-40%.

Olga: What do these failure rates and the high level of ambiguity tell us about the grammar?

Dan: There might not necessarily be a problem here, especially if we were to use techniques that cut up the search space into smaller sub-problems, e.g. Ewa Muszynska’s work on chunking. But when parsing there are ‘black holes’ consisting of dense groupings of chart edges. The grammarian needs better tools to identify and explore these places.

Stephan: Expressed the hope that DELPH-IN is viewed externally not just as treebank producers. He would like to see more research in DELPH-IN into the core parsing problem. Shouldn’t our parsers take advantage of recent deep learning approaches to improve tagging, for instance? Our current technology for tagging unknown words seems particularly inaccurate.

Dan: Talks about how he is convinced there are certain very fundamental things that could be discovered through exploring massive ambiguity, but our current tools do not really allow us to investigate such issues in million-edge parse charts. And it is not that the chart does not load (it does eventually), it is more a visualization issue; there is nothing useful that can be done with that chart.

Woodley: In the past, didn’t you dive into the chart but always find some way of justifying any particular edge being there?

Dan: No, there’s always something there that shouldn’t be. Sometimes a type could be changed to stop such edges being created, but fixes of this kind never ended up having any significant effect on overall performance.

Glenn: In that case, it seems likely there is no silver bullet.

Dan: There might in some cases be 5-6 things that mutually interact to inflate edge numbers. And extra edges can multiply out. Some 20-30 token sentences unexpectedly blow up.

John: In certain scenarios, might beam search be acceptable instead of full forest parsing? Beam search would not be appropriate a some points in grammar development, but it could be worth considering. There’s a very recent TACL paper by Meister, Vieira & Cotterell https://arxiv.org/abs/2007.03909 reporting promising results.

Alexandre: What do you actually see when you look at dense parts of the chart?

Dan: When sampling chart cells with many edges, it’s often possible to explain why each edge should be there given some context or other, in which case it’s not possible to find a way of getting rid of it. There are still pathological problems – even for parsing WSJ sentences. Beam search seems tempting, but there would need to be a test harness to control how it is applied. Some rules such as vocative cost a lot but are very little used in genres such as WSJ. Maybe a one-size fits all grammar and set of parameters aren’t appropriate.

Stephan: Rebecca Dridan got some excellent results for supertagging. However, since then we have not managed to reliably package up this technology. How about applying the latest neural approaches to lexical type prediction and pruning? For some tasks these kinds of models achieve super-human accuracies.

Dan: It’s not clear what super-human performance means for tagging, since it’s not a task that humans do. At present, after making only a few changes in the grammar, the supertagger needs to be retrained; it is brittle. Retraining is expensive. It would be fine if grammar resources were static but unfortunately they’re not.

Berthold: parsing bottom up does not work so well when syntactic information that constrains the analysis is available only at the top of the tree, e.g. partial VP fronting in German. He has tried all available computational tweaks, but none of these addressed the basic problem that parsing is very expensive if the valency of the main verb is not known until the end of the parse. Although CFG approximation seemed promising, it was not the solution to top-down prediction. He couldn’t get supertagging to work well with German’s richer morphology.

Dan: There is a PCFG derived from the ERG, but it’s so large that it costs a lot to apply. It was designed for robust parsing, but it could potentially be used to guide beam search or top-down prediction.

Olga: I’m afraid we’re out of time. Thanks to all for the interesting discussion.

Last update: 2020-07-15 by JohnCarroll [edit]