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| mtool | |
| ===== | |
| <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Flag_of_Switzerland.svg/240px-Flag_of_Switzerland.svg.png" width=20> **The Swiss Army Knife of Meaning Representation** | |
| This repository provides software to support participants in the | |
| shared tasks on [Meaning Representation Parsing (MRP)](http://mrp.nlpl.eu) | |
| at the | |
| [2019](http://www.conll.org/2019) and | |
| [2020 Conference on Computational Natural Language Learning](http://www.conll.org/2020) (CoNLL). | |
| Please see the above task web site for additional background. | |
| Scoring | |
| ------- | |
| `mtool` implements the official MRP 2019 cross-framwork metric, as well as | |
| a range of framework-specific graph similarity metrics, viz. | |
| + MRP (Maximum Common Edge Subgraph Isomorphism); | |
| + EDM (Elementary Dependency Match; [Dridan & Oepen, 2011](http://aclweb.org/anthology/W/W11/W11-2927.pdf)); | |
| + SDP Labeled and Unlabeled Dependency F1 ([Oepen et al., 2015](http://aclweb.org/anthology/S/S14/S14-2008.pdf)); | |
| + SMATCH Precision, Recall, and F1 ([Cai & Knight, 2013](http://www.aclweb.org/anthology/P13-2131)); | |
| + UCCA Labeled and Unlabeled Dependency F1 ([Hershcovich et al., 2019](https://www.aclweb.org/anthology/S19-2001)). | |
| The ‘official’ cross-framework metric for the MRP 2019 shared task is a generalization | |
| of the framework-specific metrics, considering all applicable ‘pieces of information’ (i.e. | |
| tuples representing basic structural elements) for each framework: | |
| 1. top nodes; | |
| 2. node labels; | |
| 3. node properties; | |
| 4. node anchoring; | |
| 5. directed edges; | |
| 6. edge labels; and | |
| 7. edge attributes. | |
| When comparing two graphs, node-to-node correspondences need to be established (via a | |
| potentially approximative search) to maximize the aggregate, unweighted score of all of the tuple | |
| types that apply for each specific framework. | |
| Directed edges and edge labels, however, are always considered in conjunction during | |
| this search. | |
| ``` | |
| ./main.py --read mrp --score mrp --gold data/sample/eds/wsj.mrp data/score/eds/wsj.pet.mrp | |
| {"n": 87, | |
| "tops": {"g": 87, "s": 87, "c": 85, "p": 0.9770114942528736, "r": 0.9770114942528736, "f": 0.9770114942528736}, | |
| "labels": {"g": 2500, "s": 2508, "c": 2455, "p": 0.9788676236044657, "r": 0.982, "f": 0.9804313099041533}, | |
| "properties": {"g": 262, "s": 261, "c": 257, "p": 0.9846743295019157, "r": 0.9809160305343512, "f": 0.982791586998088}, | |
| "anchors": {"g": 2500, "s": 2508, "c": 2430, "p": 0.9688995215311005, "r": 0.972, "f": 0.9704472843450479}, | |
| "edges": {"g": 2432, "s": 2439, "c": 2319, "p": 0.95079950799508, "r": 0.9535361842105263, "f": 0.952165879696161}, | |
| "attributes": {"g": 0, "s": 0, "c": 0, "p": 0.0, "r": 0.0, "f": 0.0}, | |
| "all": {"g": 7781, "s": 7803, "c": 7546, "p": 0.9670639497629117, "r": 0.9697982264490426, "f": 0.9684291581108829}} | |
| ``` | |
| Albeit originally defined for one specific framework (EDS, DM and PSD, AMR, or UCCA, respectively), | |
| the pre-MRP metrics are to some degree applicable to other frameworks too: the unified MRP representation | |
| of semantic graphs enables such cross-framework application, in principle, but this functionality | |
| remains largely untested (as of June 2019). | |
| The `Makefile` in the `data/score/` sub-directory shows some example calls for the MRP scorer. | |
| As appropriate (e.g. for comparison to third-party results), it is possible to score graphs in | |
| each framework using its ‘own’ metric, for example (for AMR and UCCA, respectively): | |
| ``` | |
| ./main.py --read mrp --score smatch --gold data/score/amr/test1.mrp data/score/amr/test2.mrp | |
| {"n": 3, "g": 30, "s": 29, "c": 24, "p": 0.8, "r": 0.8275862068965517, "f": 0.8135593220338982} | |
| ``` | |
| ``` | |
| ./main.py --read mrp --score ucca --gold data/score/ucca/ewt.gold.mrp data/score/ucca/ewt.tupa.mrp | |
| {"n": 3757, | |
| "labeled": | |
| {"primary": {"g": 63720, "s": 62876, "c": 38195, | |
| "p": 0.6074654876264394, "r": 0.5994193345888261, "f": 0.6034155897500711}, | |
| "remote": {"g": 2673, "s": 1259, "c": 581, | |
| "p": 0.4614773629864972, "r": 0.21735877291432848, "f": 0.2955239064089522}}, | |
| "unlabeled": | |
| {"primary": {"g": 56114, "s": 55761, "c": 52522, | |
| "p": 0.9419128064417783, "r": 0.9359874541112735, "f": 0.938940782122905}, | |
| "remote": {"g": 2629, "s": 1248, "c": 595, | |
| "p": 0.47676282051282054, "r": 0.22632179535945227, "f": 0.3069383543977302}}} | |
| ``` | |
| For all scorers, the `--trace` command-line option will enable per-item scores in the result | |
| (indexed by frameworks and graph identifiers). | |
| For MRP and SMATCH, the `--limit` option controls the maximum node pairing steps or | |
| hill-climbing iterations, respectively, to attempt during the search (with defaults `500000` | |
| and `20`, respectively). | |
| As of early July, 2019, the search for none-to-node correspondences in the MRP metric can be | |
| initialized from the result of the random-restart hill-climbing (RRHC) search from SMATCH. | |
| This initialization is on by default; it increases running time of the MRP scorer but yields | |
| a guarantee that the `"all"` counts of matching tuples in MRP will always be at least as | |
| high as the number of `"c"`(orrect) tuples identified by SMATCH. | |
| To control the two search steps in MRP computation separately, the `--limit` option can | |
| take a colon-separated pair of integers, for example `5:100000` for five hill-climbing | |
| iterations and up to 100,000 node pairing steps. | |
| Note that multi-valued use of the `--limit` option is only meaningful in conjunction | |
| with the MRP metric, and that setting either of the two values to `0` will disable the | |
| corresponding search component. | |
| Finally, the MRP scorer can parallelize evaluation: an option like `--cores 8` (on | |
| suitable hardware) will run eight `mtool` processes in parallel, which should reduce | |
| scoring time substantially. | |
| Analytics | |
| --------- | |
| [Kuhlmann & Oepen (2016)](http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00268) discuss a range of structural graph statistics; `mtool` integrates their original code, e.g. | |
| ``` | |
| ./main.py --read mrp --analyze data/sample/amr/wsj.mrp | |
| (01) number of graphs 87 | |
| (02) number of edge labels 52 | |
| (03) \percentgraph\ trees 51.72 | |
| (04) \percentgraph\ treewidth one 51.72 | |
| (05) average treewidth 1.494 | |
| (06) maximal treewidth 3 | |
| (07) average edge density 1.050 | |
| (08) \percentnode\ reentrant 4.24 | |
| (09) \percentgraph\ cyclic 13.79 | |
| (10) \percentgraph\ not connected 0.00 | |
| (11) \percentgraph\ multi-rooted 0.00 | |
| (12) percentage of non-top roots 0.00 | |
| (13) average edge length -- | |
| (14) \percentgraph\ noncrossing -- | |
| (15) \percentgraph\ pagenumber two -- | |
| ``` | |
| Validation | |
| ---------- | |
| `mtool` can test high-level wellformedness and (superficial) plausiblity of MRP | |
| graphs through its emerging `--validate` option. | |
| The MRP validator continues to evolve, but the following is indicative of its | |
| functionality: | |
| ``` | |
| ./main.py --read mrp --validate all data/validate/eds/wsj.mrp | |
| validate(): graph ‘20001001’: missing or invalid ‘input’ property | |
| validate(): graph ‘20001001’; node #0: missing or invalid label | |
| validate(): graph ‘20001001’; node #1: missing or invalid label | |
| validate(): graph ‘20001001’; node #3: missing or invalid anchoring | |
| validate(): graph ‘20001001’; node #6: invalid ‘anchors’ value: [{'from': 15, 'to': 23}, {'from': 15, 'to': 23}] | |
| validate(): graph ‘20001001’; node #7: invalid ‘anchors’ value: [{'form': 15, 'to': 17}] | |
| ``` | |
| Conversion | |
| ---------- | |
| Among its options for format coversion, `mtool` supports output of graphs to the | |
| [DOT language](https://www.graphviz.org/documentation/) for graph visualization, e.g. | |
| ``` | |
| ./main.py --id 20001001 --read mrp --write dot data/sample/eds/wsj.mrp 20001001.dot | |
| dot -Tpdf 20001001.dot > 20001001.pdf | |
| ``` | |
| When converting from token-based file formats that may lack either the underlying | |
| ‘raw’ input string, character-based anchoring, or both, the `--text` command-line | |
| option will enable recovery of inputs and attempt to determine anchoring. | |
| Its argument must be a file containing pairs of identifiers and input strings, one | |
| per line, separated by a tabulator, e.g. | |
| ``` | |
| ./main.py --id 20012005 --text data/sample/wsj.txt --read dm --write dot data/sample/psd/wsj.sdp 20012005.dot | |
| ``` | |
| For increased readability, the `--ids` option will include MRP node identifiers | |
| in graph rendering, and the `--strings` option can replace character-based | |
| anchors with the corresponding sub-string from the `input` field of the graph | |
| (currently only for the DOT output format), e.g. | |
| ``` | |
| ./main.py --n 1 --strings --read mrp --write dot data/sample/ucca/wsj.mrp vinken.dot | |
| ``` | |
| Diagnostics | |
| -------------- | |
| When scoring with the MRP metric, `mtool` can optionally provide a per-item | |
| breakdown of differences between the gold and the system graphs, i.e. record | |
| false negatives (‘missing’ tuples) and false positives (‘surplus’ ones). | |
| This functionality is activated via the `--errors` command-line option, and | |
| tuple mismatches between the two graphs are recorded as a hierarchically | |
| nested JSON object, indexed (in order) by framework, item identifier, and tuple | |
| type. | |
| For example: | |
| ``` | |
| ./main.py --read mrp --score mrp --framework eds --gold data/score/lpps.mrp --errors errors.json data/score/eds/lpps.peking.mrp | |
| ``` | |
| For the first EDS item (`#102990`) in this comparison, `errors.json` will | |
| contain a sub-structure like the following: | |
| ``` | |
| {"correspondences": [[0, 0], [1, 1], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], | |
| [11, 12], [12, 13], [13, 15], [14, 16], [15, 17], [16, 14], [17, 18], [18, 19], [19, 20]], | |
| "labels": {"missing": [[2, "_very+much_a_1"]], | |
| "surplus": [[3, "_much_x_deg"], [2, "_very_x_deg"]]}, | |
| "anchors": {"missing": [[2, [6, 7, 8, 9, 11, 12, 13, 14]]], | |
| "surplus": [[2, [6, 7, 8, 9]], [3, [11, 12, 13, 14]]]}, | |
| "edges": {"surplus": [[2, 3, "arg1"]]}} | |
| ``` | |
| When interpreting this structure, there are (of course) two separate spaces of | |
| node identifiers; the `correspondences` vector records the (optimal) | |
| node-to-node relation found by the MRP scorer, pairing identifiers from the | |
| *gold* graph with corresponding identifiers in the *system* graph. | |
| In the above, for example, gold node `#2` corresponds to system node `#3`, | |
| and there is a spurious node `#2` in the example system graph, which | |
| does not correspond to any of the gold nodes. | |
| Node identifiers in `"missing"` entries refer to gold nodes, whereas | |
| identifiers in `"surplus"` entries refer to the system graph, and they may | |
| or may not stand in a correspondence relation to a gold node. | |
| The differences between these two graphs can be visualized as follows, color-coding | |
| false negatives in red, and false positives in blue | |
| (and using gold identifiers, where available). | |
|  | |
| Common Options | |
| -------------- | |
| The `--read` and `--write` command-line options determine the input and output | |
| codecs to use. | |
| Valid input arguments include `mrp`, `amr`, `ccd`, `dm`, `eds`, `pas`, `psd`, `ud`, `eud`, | |
| and `ucca`; note that some of these formats are only [partially supported](https://github.com/cfmrp/mtool/issues). | |
| The range of supported output codecs includes `mrp`, `dot`, or `txt`. | |
| The optional `--id`, `--i`, or `--n` options control which graph(s) | |
| from the input file(s) to process, selecting either by identifier, by (zero-based) | |
| position into the sequence of graphs read from the file, or using the first _n_ | |
| graphs. | |
| These options cannot be combined with each other and take precendence over each | |
| other in the above order. | |
| Another way of selecting only a subset of graphs (from both the gold and | |
| system inputs) is the `--framework` option, which will limit the selection | |
| to graphs with matching `"framework"` values. | |
| Finally, the `--unique` option will discard graphs with multiple occurences | |
| of the same identifier, keeping only the first occurence from the input stream. | |
| Most top-level graph properties (`"id"`, `"time"`, `"source"`, `"provenance"`, | |
| `"language"`, `"flavor"`, `"framework"`, `"targets"`, `"input"`) can be set | |
| (or destructively overwritten, upon completion of input processing) using the | |
| `--inject` option, which takes as its argument a JSON object, e.g. | |
| ``` | |
| ./main.py --text wsj.txt --read eds \ | |
| --inject '{"source": "wsj", "provenance": "Redwoods Ninth Growth (ERG 1214)"}' \ | |
| --write mrp wsj.eds wsj.mrp | |
| ``` | |
| Installation | |
| ------------ | |
| You can install `mtool` via `pip` with the following command: | |
| ``` | |
| pip install git+https://github.com/cfmrp/mtool.git#egg=mtool | |
| ``` | |
| Authors | |
| ------- | |
| + Daniel Hershcovich <daniel.hershcovich@gmail.com> (@danielhers) | |
| + Marco Kuhlmann <marco.kuhlmann@liu.se> (@khlmnn) | |
| + Stephan Oepen <oe@ifi.uio.no> (@oepen) | |
| + Tim O'Gorman <timjogorman@gmail.com> (@timjogorman) | |
| Contributors | |
| ------------ | |
| + Yuta Koreeda <koreyou@mac.com> (@koreyou) | |
| + Matthias Lindemann <mlinde@coli.uni-saarland.de> (@namednil) | |
| + Hiroaki Ozaki <taryou.ozk@gmail.com> (@taryou) | |
| + Milan Straka <straka@ufal.mff.cuni.cz> (@foxik) | |
| [](https://travis-ci.org/cfmrp/mtool) | |
| [](https://ci.appveyor.com/project/danielh/mtool) | |