SlowGuess/ACL_2020_OCR huggingface dataset:
in both directions and the inputs are concatenations of 100-dimensional randomly-initialized word embeddings with the final hidden vectors of 256-dimensional single-layer character-based bi-LSTMs; for BERT, we use pre-trained cased multi-lingual BERT models $^{11}$  and fine-tune the weights. We adopt the parameter settings of Dozat and Manning (2017) and use 500 and 100 dimensions for  $U^{\mathrm{att}}$  and  $U_r^{\mathrm{rel}}$ , respectively. The MLP in the taggers have 500 hidden dimensions. We use a dropout (Srivastava et al., 2014) rate of 0.33, a single hidden layer, and a ReLU activation function (Nair and Hinton, 2010) for all MLPs. The models are trained with the Adam optimizer (Kingma and Ba, 2015) using a batch size of 16 sentences. The learning rates are set to  $1e^{-3}$  for bi-LSTMs and  $1e^{-5}$  for BERT initially and then multiplied by a factor of 0.1 if the performance on the development set stops improving within 3200 training iterations. For the parsing models, we use the projective Eisner (1996) decoder algorithm. For the joint training and joint decoding models, we tune  $\lambda \in \{0.02, 0.05, 0.1, 0.3, 0.5, 0.9\}$  for each treebank independently and fix the settings based on the best dev-set scores. We run each model with 5 different random seeds and report the mean and standard deviation for each setting.
Results We report F1 scores based on multi-word headless-structure extraction. Table 3 compares different strategies for identifying headless MWEs in parse trees. Tagging is consistently better than parsing except for two treebanks with BERT feature extractor. Tagging beats parsing in all but two combinations of treebank and feature extractor. As hypothesized, our joint decoder improves over both strategies by  $0.69\%$ $(1.64\%)$  absolute through combined decisions from parsing and tagging with(out)
$^{11}$ https://github.com/huggingface/transformers
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w/bi-LSTM Treebank	Compl. Ratio ↓	Parsing	Tagging	MTL Parsing	Tagging	Joint Decoding
English	100.00	91.24±0.60	91.81±0.45	93.00±0.83	93.24±0.76	93.49±0.43
nl_alpino	100.00	72.66±1.73	74.94±1.00	77.29±0.80	75.58±1.18	79.65±1.05
nl_lassysmall	99.82	76.44±1.56	77.98±1.56	78.13±0.98	77.58±1.17	78.92±1.00
no_nynorsk	99.78	85.34±0.81	87.67±0.90	86.72±0.76	87.44±0.76	88.40±0.39
pt_bosque	97.38	89.55±1.10	90.97±0.46	91.30±0.75	92.07±1.04	90.63±1.56
it_postwita	94.89	75.35±1.05	76.37±1.72	78.46±1.08	77.87±0.57	78.38±1.04
de_gsd	93.00	63.32±1.36	64.10±1.31	64.81±2.05	65.07±1.35	65.86±1.34
Average	79.13	80.55	81.39	81.26	82.19
w/BERT Treebank	Compl. Ratio ↓	Parsing	Tagging	MTL Parsing	Tagging	Joint Decoding
English	100.00	94.98±0.26	95.45±0.23	95.01±0.20	95.86±0.19	95.51±0.58
nl_alpino	100.00	83.87±1.61	83.32±1.01	84.65±1.48	85.90±1.51	86.61±1.52
nl_lassysmall	99.82	87.16±1.20	87.52±0.59	88.10±0.80	87.68±0.78	88.35±0.49
no_nynorsk	99.78	92.16±0.93	93.48±0.48	92.45±0.34	93.11±0.21	93.08±0.62
pt_bosque	97.38	92.98±0.82	93.47±0.55	93.42±0.65	93.85±0.57	94.01±0.19
it_postwita	94.89	80.80±1.51	80.80±1.52	80.90±1.78	81.33±0.43	80.83±1.20
de_gsd	93.00	68.21±1.43	70.28±0.70	70.04±1.14	71.05±1.12	70.72±0.90
Average	85.74	86.33	86.37	86.97	87.02
Table 3: Flat-structure identification test-set F1 scores (\%) with bi-LSTM (top) and BERT (bottom). The cell with the best result for each treebank has blue shading; results within one standard deviation of the best are bolded.
BERT. We also compare the joint decoding setting with MTL training strategy alone. While joint decoding yields superior F1 scores, MTL is responsible for a large portion of the gains: it accounts for over half of the average gains with bi-LSTMs, and when we use pre-trained BERT feature extractors, the accuracies of jointly-trained taggers are essentially as good as joint decoding models.
Interestingly, the choice of feature extractors also has an effect on the performance gap between parsers and taggers. With bi-LSTMs, tagging is  $1.42\%$  absolute F1 higher than parsing, and the gap is mitigated through MTL. While pre-trained BERT reduces the performance difference dramatically down to  $0.59\%$  absolute, MTL no longer helps parsers overcome this gap. Additionally, we observe that MTL helps both parsing and tagging models, demonstrating that the two views of the same underlying structures are complementary to each other and that learning both can be beneficial to model training. By resolving such representational discrepancies, joint decoding exhibits further accuracy improvement.
In terms of dependency parsing accuracies, we
confirm that our parsing-only models achieve state-of-the-art performance on the UD treebanks, but there are no significant differences in parsing results among parsing-only, MTL and jointly-decoded models. See Appendix for detailed results.
Syntactic analysis in conjunction with MWE identification is an important line of research (Wehrli, 2000). The span-based representations that form the basis of phrase-structure trees (as opposed to dependency trees) are arguably directly compatible with headless spans. This motivates approaches using joint constituency-tree representations based on context-free grammars (Arun and Keller, 2005; Constant et al., 2013) and tree substitution grammars (Green et al., 2011, 2013). Finkel and Manning (2009) add new phrasal nodes to denote named entities, enabling statistical parsers trained on this modified representation to produce both parse trees and named entity spans simultaneously. Le Roux et al. (2014) use dual decomposition to develop a joint system that combines phrase-structure parsers and taggers for compound recognition. These ap
proaches do not directly transfer to dependency-based representations since dependency trees do not explicitly represent phrases.
In the context of dependency parsing, Eryiğit et al. (2011) report that MWE annotations have a large impact on parsing. They find that the dependency parsers are more accurate when MWE spans are not unified into single lexical items. Similar to the phrase-structure case, Candito and Constant (2014) consider MWE identification as a side product of dependency parsing into joint representations. This parse-then-extract strategy is widely adopted (Vincze et al., 2013; Nasr et al., 2015; Simko et al., 2017). Waszczuk et al. (2019) introduce additional parameterized scoring functions for the arc labelers and use global decoding to produce consistent structures during arc-labeling steps once unlabeled dependency parse trees are predicted. Our work additionally proposes a joint decoder that combines the scores from both parsers and taggers. Alternative approaches to graph-based joint parsing and MWE identification include transition-based (Constant and Nivre, 2016) and easy-first (Constant et al., 2016) dependency parsing. These approaches typically rely on greedy decoding, whereas our joint decoder finds the globally optimal solution through dynamic programming.
Our work only focuses on a subset of MWEs that do not have internal structures. There is substantial research interest in the broad area of MWEs (Sag et al., 2002; Constant et al., 2017) including recent releases of datasets (Schneider and Smith, 2015), editions of shared tasks (Savary et al., 2017; Ramisch et al., 2018) and workshops (Savary et al., 2018, 2019). We leave it to future work to extend the comparison and combination of taggers and dependency parsers to other MWE constructions.
6 Conclusion and Further Directions
Our paper provides an empirical comparison of different strategies for extracting headless MWEs from dependency parse trees: parsing, tagging, and joint modeling. Experiments on the MWE-Aware English Dependency Corpus and UD 2.2 across five languages show that tagging, a widely-used methodology for extracting spans from texts, is more accurate than parsing for this task. When using bi-LSTM (but not BERT) representations, our proposed joint decoder reaches higher F1 scores than either of the two other strategies, by combining scores of the two different and complementary
representations of the same structures. We also show that most of the gains stem from a multi-task learning strategy that shares common neural representations between the parsers and the taggers.
An interesting additional use-case for our joint decoder is when a downstream task, e.g., relation extraction, requires output structures from both a parser and a tagger. Our joint decoder can find the highest-scoring consistent structures among all candidates, and thus has the potential to provide simpler model designs in downstream applications.
Our study has been limited to a few treebanks in UD partially due to large variations and inconsistencies across different treebanks. Future community efforts on a unified representation of flat structures for all languages would facilitate further research on linguistically-motivated treatments of headless structures in "headful" dependency treebanks.
Another limitation of our current work is that our joint decoder only produces projective dependency parse trees. To handle non-projectivity, one possible solution is pseudo-projective parsing (Nivre and Nilsson, 2005). We leave it to future work to design a non-projective decoder for joint parsing and headless structure extraction.
We thank the three anonymous reviewers for their comments, and Igor Malioutov, Ana Smith and the Cornell NLP group for discussion and comments. TS was supported by a Bloomberg Data Science Ph.D. Fellowship.
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