SlowGuess/NAACL_2022_OCR huggingface dataset:
Young-Suk Lee†, Ramón Fernandez Astudillo†, Thanh Lam Hoang‡, Tahira Naseem†, Radu Florian†, Salim Roukos†
{ysuklee,tanseem,raduf,roukos}@us.ibm.com
ramon. astudillo@ibm.com
t.l.hoang@ie.ibm.com
IBM Research AI†, IBM Research - Ireland‡
AMR parsing has experienced an unprecedented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning. Self-learning techniques have also played a role in pushing performance forward. However, for most recent high performant parsers, the effect of self-learning and silver data augmentation seems to be fading. In this paper we propose to overcome this diminishing returns of silver data by combining Smatch-based assembling techniques with ensemble distillation. In an extensive experimental setup, we push single model English parser performance to a new state-of-the-art, 85.9 (AMR2.0) and 84.3 (AMR3.0), and return to substantial gains from silver data augmentation. We also attain a new state-of-the-art for cross-lingual AMR parsing for Chinese, German, Italian and Spanish. Finally we explore the impact of the proposed technique on domain adaptation, and show that it can produce gains rivaling those of human annotated data for QALD-9 and achieve a new state-of-the-art for BioAMR.
Adoption of the Transformer architecture (Vaswani et al., 2017) for Abstract Meaning Representation (AMR) parsing (Cai and Lam, 2020; Fernandez Astudillo et al., 2020) as well as pretrained language models (Bevilacqua et al., 2021; Zhou et al., 2021b; Bai et al., 2022) have enabled an improvement of above 10 Smatch points (Cai and Knight, 2013), the standard metric, in the last two years.
Data augmentation techniques have also shown great success in pushing the state-of-the-art of AMR parsing forward. These include generating silver AMR annotations with a trained parser (Konstas et al., 2017; van Noord and Bos, 2017), with multitask pre-training and fine-tuning (Xuet al., 2020) as well as combining AMR to source text and silver AMR generation (Lee et al., 2020)
and stacked pre-training of silver data from different models – from low performance to high performance silver data (Xia et al., 2021). However, the latest BART-based state-of-the-art parsers, have shown diminishing returns for data augmentation. Both SPRING (Bevilacqua et al., 2021) and Structured-BART (Zhou et al., 2021b) gain a mere 0.5 Smatch from self-learning, compared with over 1 point gains of the previous, less performant, models. Since performance scores are already above where inter annotator agreement (IAA) is assumed to be, i.e. 83 for newswire and 79 for web text reported in (Banarescu et al., 2013), one possible explanation is that we are reaching some unavoidable performance plateau.
In this work we show that we can achieve significant performance gains close to 2 Smatch point with the newly proposed data augmentation technique, contrary to the results from the previous state-of-the-art systems. The main contributions of this paper are as follows:
- We propose to combine Smatch-based model ensembling (Barzdins and Gosko, 2016; Hoang et al., 2021) and ensemble distillation (Hinton et al., 2015) of heterogeneous parsers to produce high quality silver data.
- We offer a Bayesian ensemble interpretation of this technique as alternative to views such as Minimum Bayes Risk decoding (Goel and Byrne, 2000) and name the technique Maximum Bayes Smatch Ensemble (MBSE).
- Applied to English monolingual parsing, MBSE distillation yields a new single system state-of-the-art (SoTA) on AMR2.0 (85.9) and AMR3.0 (84.3) test sets.
- Trained with Structured-mBART<sup>1</sup>, it yields new SoTA for Chinese (63.0), German (73.7),
<sup>1</sup>https://github.com/IBM/transition-amr-parser/tree/structured-mbart
Human Language Technologies, pages 5379 - 5392
Italian (76.1) and Spanish (77.1) cross-lingual parsing.
- Applied to domain adaptation, MBSE distillation achieves the performance comparable to human annotations of QALD-9 training data and achieves new SoTA on BioAMR test set.
- We release QALD-9-AMR treebank $^2$  at, which comprises 408 training and 150 test sentences.
2 Maximum Bayes Smatch Ensemble
Ensemble distillation (Hinton et al., 2015) integrates knowledge of different teacher models into a student model. For sequence to sequence models, e.g. machine translation, it is possible to ensemble models by combining probabilities of words given context at each time step (Kim and Rush, 2016; Freitag et al., 2017). Syntactic and semantic parsers model a distribution over graphs that is harder to integrate across teacher models in an optimal way. For particular cases like dependency parsing, it is possible to ensemble teachers based on the notion of edge attachment (Kuncoro et al., 2016), which is related to the usual evaluation metric, Label Attachment Score (LAS). However, AMR graphs are quite complex and not explicitly aligned to words. The standard Smatch (Cai and Knight, 2013) metric approximates the NP-Complete problem of aligning nodes across graphs with a hill climbing algorithm. This illustrates the difficulty of achieving consensus across teachers for AMR ensembling.
Prior work ensembling AMR graphs has leveraged Smatch directly or its hill climbing strategy for ensembling. The ensemble in (Barzdins and Gosko, 2016) selects, among a number of candidate AMRs, the one that has the largest average Smatch with respect to all sampled AMRs. The ensemble in (Hoang et al., 2021), modifies the candidate AMRs to increase consensus as measured by coverage. Then it selects from the union of original and modified graphs for the one with highest coverage or largest average Smatch. One possible interpretation of both techniques is that of Minimum Bayes Risk (MBR) decoding, a well established method in Automatic Speech Recognition (ASR) (Goel and Byrne, 2000) and Machine Translation (MT) (Kumar and Byrne, 2004). Assuming that we have a model predicting a graph from an input
sentence  $p(g \mid w)$ , normal decoding entails searching among model outputs  $g$  for the one that has the highest likelihood according to the model  $p(g \mid w)$ . MBR searches instead for the model output that minimizes the risk with respect to the distribution of possible human (gold) outputs for a given input
$$
\hat {g} = \arg \min  _ {g} \left\{E _ {p (g ^ {h} | w)} \left\{R (g, g ^ {h}) \right\} \right\}
$$
where  $p(g^h \mid w)$  is the distribution of correct human outputs, e.g. given by multiple annotators, and  $R$  is a risk function that measures how severe deviations from  $g^h$  are. In this case risk would be minus Smatch. Since in practice  $p(g^h \mid w)$  is not available, MBR takes often the strong assumption of replacing  $p(g^h \mid w)$  by the model distribution itself  $p(g \mid w)$ .
Here we suggest another Bayesian interpretation, that requires less strong assumptions than MBR, a Bayesian model ensemble (Wilson and Izmailov, 2020). Indeed techniques above can be seen as solving
$$
\hat {g} = \arg \max _ {g \in \mathcal {G}} \{E _ {p (\mathcal {M} | \mathcal {D})} \{\mathrm {S m a t c h} (g, \tilde {g} _ {\mathcal {M}}) \} \}
$$
where  $p(\mathcal{M} \mid \mathcal{D})$  is the distribution of models  $\mathcal{M}$  given training data  $\mathcal{D}$ , approximated by a sample average of models of different architectures or different random seeds, and
$$
\tilde {g} _ {\mathcal {M}} = \mathrm {p o s t} \left(\arg \max _ {y} \left\{\prod_ {t = 1} ^ {| y |} p _ {\mathcal {M}} (y _ {t} \mid y _ {<   t}, w) \right\}\right)
$$
is the output of a conventional decoding process for each parser prediction distribution  $p_{\mathcal{M}}$ , including post-processing post(). This process differs across models indexed by  $\mathcal{M}$ , for example  $y$  can be transition actions or linearized graphs and post() running the state-machine or linearized graph post-processing<sup>3</sup>.  $\mathcal{G}$  is the space of candidate graphs, which in Barzdins and Gosko (2016) are the AMRs resulting from decoding each sample from  $p(\mathcal{M} \mid \mathcal{D})$  and in Hoang et al. (2021) are those same graphs plus the modified pivot graphs. There is in principle no restriction on how to build the set  $\mathcal{G}$ . Decoding a graph  $g \in \mathcal{G}$  means here selecting the member of that set maximizing the expected Smatch and is different from each parser's decoding process.
3We consider only auto-regressive models in this work but this approach could also encompass e.g. graph-based parsers.
$^{2}$ https://github.com/IBM/AMR-annotations
Figure 1: Data augmentation framework: Given a labeled example in English  $(\mathbf{e}_1, \mathbf{g}_1)$ , we use an AMR-to-text generation system to generate an alternative input text  $\hat{\mathbf{e}}_2$  for  $\mathbf{g}_1$  following (Lee et al., 2020). Given a sentence  $\mathbf{e}_3$ , and various state-of-the-art off-the-shelf parser outputs  $(A, B, C)$ , Maximum Bayes Smatch Ensemble (MBSE) produces a single annotation for each input sentence by selecting from existing AMRs or their modified versions. MBSE is only applied to unlabeled English sentences to produce  $\hat{\mathbf{g}}_3$ . Following (Damonte and Cohen, 2018), we translate the English sentences to e.g. Spanish, to yield new training samples  $(\hat{\mathbf{s}}_1, \mathbf{g}_1)$ ,  $(\hat{\mathbf{s}}_2, \mathbf{g}_1)$ ,  $(\hat{\mathbf{s}}_3, \hat{\mathbf{g}}_3)$  to train a Spanish cross-lingual parser. We use the English pairs  $(\mathbf{e}_1, \mathbf{g}_1)$ ,  $(\hat{\mathbf{e}}_2, \mathbf{g}_1)$ ,  $(\mathbf{e}_3, \hat{\mathbf{g}}_3)$  to train an English parser.
If we replace  $\mathrm{Smatch}()$  by an indicator function on the decoding outputs  $1_{g = \tilde{g}_{\mathcal{M}}}$ , then
$$
\hat {g} = \arg \max _ {g \in \mathcal {G}} \{E _ {p (\mathcal {M} | \mathcal {D})} \{1 _ {g = \tilde {g} _ {\mathcal {M}}} \} \}
$$
recovers majority voting of AMR graphs. Since the space of graphs is exponentially large on the input size, this would be too sparse to attain meaningful vote counts. The propagation of the uncertainty in  $p(\mathcal{M} \mid \mathcal{D})$  through the Smatch() transformation both solves the sparsity problem, and allows optimization on a space that is better related to the target metric. The method will be henceforth described here as Maximum Bayes Smatch Ensemble distillation (MBSE distillation).
In what follows, we will consider three versions for ensembling, the Smatch version of Hoang et al. (2021) (graphene-Smatch), the average-Smatch selection of Barzdins and Gosko (2016), and a greedy version of Barzdins and Gosko (2016) where we select the two highest Smatch AMRs and from that pair, keep the graph with the highest Smatch with respect to the remaining graphs (greedy-select). The greedy-select algorithm is given in Algorithm 1 of Appendix A and performs similarly to the average-Smatch of Barzdins and Gosko (2016).
3 Silver Training Strategy
We now describe the AMR silver training strategy proposed in this work. This strategy creates high quality English and cross-lingual AMR annotations for unlabeled data with MBSE and alternative input sentences of gold AMRs via AMR-to-text.
As depicted in Fig. 1, we start with 1) a set of gold-labeled (English sentence, AMR) pairs,
2) a set of unlabeled English sentences and 3) pre-trained English-to-foreign language Machine Translation systems. Assuming  $N$  off-the-shelf AMR parsers, we train each of the  $N$  parsers using the gold data with their respective training procedure. More than one random seed may be trained for some parsers, leading to more than  $N$  AMR parses for each input sentence.
After the parsers have been trained, we use them to parse the unlabeled English text as in Konstas et al. (2017). Interpreting the set of trained models as samples of the model distribution, we apply the MBSE distillation methods described in Section 2. We apply all variations of the MBSE algorithms including graphene-Smatch, greedy-select and average-Smatch algorithms.
For English parsers, the MBSE distilled AMR annotations are added to the human-annotated gold treebanks for enhanced model training. For crosslingual parsers, we translate all English input sentences to the target foreign languages and train respective cross-lingual parsers with pairs of (Foreign language input sentences, AMR graphs in English), following (Damonte and Cohen, 2018).
Following Lee et al. (2020), we also apply an AMR-to-text model (Mager et al., 2020; Ribeiro et al., 2021; Bevilacqua et al., 2021) to generate additional sentences for human-annotated AMR. We filter out the generated texts if they are too similar (BLEU  $>0.9$ ) or too dissimilar (BLEU  $<0.1$ ) to the original input texts, as measured by BLEU (Papineni et al., 2002). AMR-to-text generation is used for cross-lingual AMR parser training only.
<sup>4</sup>We use https://github.com/SapienzaNLP/spring.
For Standard Experiments	For Domain Adaptation
Dataset	Split	Sents	Tokens	Dataset	Split	Sents	Tokens
AMR2.0	Train	36,521	653K	QALD-9-AMR (new)	Train	408	3,475
Test	1,371	30K	Test	150	1,441
Dev.	1,368	29K
AMR3.0	Train	55,635	1M	Bio AMR	Train	5,452	231K
Test	1,898	39K	Test	500	22K
Dev.	1,722	37K
LP	Test	1,562	21K
PropBank	silver1	20K	386K	SQuAD2.0-Q	silverq	135K	1.5M
SQuAD2.0-C	silver1	70K	2M	BioNLP-ST-2011	silverb	15K	460K
Ontonotes5.0	silver2	59K	1.1M	CRAFT	silverb	27K	740K
WikiText-103	silver3	70K	2M	PubMed	silverb	26K	750K
Table 1: Corpus statistics for the standard benchmark experiments on AMR2.0 and AMR3.0 test sets (left) and domain adaptation experiments (right). Silver indicates the unlabeled data for silver training.
4.1 Corpus Statistics and QALD-9-AMR
Table 1 details the corpora considered for the standard benchmark experiments on AMR2.0 and AMR3.0 test sets (lef) and out-of-domain data used for domain adaptation experiments (right). Silver indicates the unlabeled data for silver AMR acquisition. SQuAD2.0-Q(questions) are for QALD-9 (silver $^q$ ) and PubMed, BioNLP-2011 (Kim et al., 2011) and CRAFT (Cohen et al., 2017) for BioAMR (silver $^b$ ).
Since there were no human annotations of QALD-9 corpus, we created QALD-9-AMR treebank. QALD-9 training/test data have been annotated by 3 skilled resident human annotators with experience in AMR annotations over a year. Each of the annotators annotated both the train and test data sets, followed by cross validation by each other. The final annotations were adjudicated by the most experienced annotator. Inter-annotator agreement (IAA) rate on a subset of 158 training sentences is over  $95\%$  in Smatch. The data is made publicly available under an Apache2 license.
4.2 Parsing Models
We use 4 off-the-shelf AMR parsers to parse un-annotated raw texts. We train the parsers following their standard configurations.
APT (Zhou et al., 2021a) $^5$  is a transition-based parser that combines hard attention over sentences with a target side action pointer mechanism to decouple source tokens from node representations
and address alignments. Cross-attention of all decoder layers is used for action-source alignment.
SPRING Bevilacqua et al. (2021) $^{6}$  fine-tunes BART (Lewis et al., 2020) to predict linearized AMR graphs, avoiding complex pipelines.
Structured-BART Zhou et al. (2021b)<sup>7</sup> models the transition-based parser state within a pre-trained BART architecture, outperforming SPRING. This is the main parser for our work.
AMRBART Bai et al. (2022)<sup>8</sup> improves the structure awareness of pre-trained BART over AMR graphs by introducing node/edge denoising and sub-graph denoising tasks, for graph-to-graph pre-training, achieving significant improvement over previous BART-based systems.
4.3 Structured-mBART
For cross-lingual AMR parsing, we adapt Structured-BART by replacing the pretrained BART with mBART of (Liu et al., 2020), henceforth Structured-mBART. The codebase is made publicly available under an Apache2 license. Structured-mBART diverges from Structured-BART mainly in input processing and vocabulary:
- For task vocabulary, Structured-mBART includes  $\sim 250\mathrm{K}$  sentencepiece tokens of (Kudo, 2018) including 25 language tags, e.g. es_XX, whereas Structured-BART includes  $\sim 50\mathrm{K}$  BPE tokens of (Sennrich et al., 2016).
- We append the source language tag to the end
$^{5}$ github.com/IBM/transition-amr-parser/tree/action-pointer, Apache2 License
$^{6}$ github.com/SapienzaNLP/spring, CC BY-NC-SA 4.0  
 $^{7}$ github.com/IBM/transition-amr-parser, Apache2 License  
 $^{8}$ https://github.com/muyeby/AMRBART, MIT License
Models	AMR2.0	AMR3.0	Q9AMR	LP	BioAMR
APT (Zhou et al., 2021a)	83.0	81.1	83.7	79.0	55.2
Structured-BART (Zhou et al., 2021b)	84.6	83.1	87.7	81.0	62.4
SPRING1 (Bevilacqua et al., 2021)	84.2	83.2	87.7	81.3	61.6
SPRING2 (Bevilacqua et al., 2021)	83.8	82.9	86.4	81.0	60.5
AMRBART (Bai et al., 2022)	85.4	84.2	88.0	82.3	63.4
aver.-Smatch (A) (Barzdins and Gosko, 2016)	86.2	84.9	89.0	82.9	64.1
graphene-Smatch (P) (Hoang et al., 2021)	86.7	85.4	89.3	83.1	65.8
greedy-select (G)	85.9	84.8	88.8	82.8	63.9
Table 2: English parsing performance in Smatch in general domain and domain adaptation for recent state-of-the-art systems (top). Performance in Smatch for the ensemble of all systems using different Smatch-based ensembling techniques (bottom). SPRING $_1$  and SPRING $_2$  are 2 random seeds of the same model. Highest scores are boldfaced.
of each input sentence without specifying the target language tag for Structured-mBART.
- For Structured-mBART, we set the learning rate to  $3\mathrm{e} - 5$  , cf.  $1\mathrm{e} - 4$  of Structured-BART, and move the layer normalization to the beginning of each transformer block.
We obtain contextualized embeddings from the pre-trained mBART for multilingual input sentence representations. For target action sequences, we map the sentencepiece tokens to the corresponding target token, by averaging all values from the sentencepiece tokens corresponding to the target token. For German, Italian and Spanish input texts, we apply the tokenizer from JAMR parser<sup>9</sup> before sentencepiece tokenization. For Chinese, we directly apply the sentencepiece tokenizer.
To explore the effect of the proposed MBSE distillation and training strategy, we consider an extensive experimental setup including standard English benchmarks (Section 5.1), cross-lingual benchmarks (Section 5.2) and out of domain data sets (Section 5.3). For model training and selection details, see Appendix B and Appendix C.
We first provide the performance evaluation of each ensembling technique used in MBSE in Table 2 to demonstrate the effectiveness of the ensemble techniques by themselves. We test the algorithm on the standard test data sets from AMR2.0 and AMR3.0 and three out-of-domain data sets, Q9AMR (QALD-9-AMR), LP (Little Prince) and
BioAMR in Table 1. We consider here only standard English AMR parsing. As expected, all MBSE algorithms, average-Smatch, graphene-Smatch and greedy-select, improve individual models by large margins. Note that while the ensembles outperform single model state-of-the-art by a large margin, the use of heterogeneous ensembles of models is computationally prohibitive in practice, both due to the cost of running different models but also the ensembling techniques.
5.1 English AMR Parsing
As displayed in Table 1, we consider the standard AMR2.0 (LDC2017T10) and AMR3.0 (LDC2020T02) treebank as gold data. For ensemble distillation, we use the data sets denoted by silver for comparison with previous work, and silver and silver to investigate the impact of unlabeled corpus size on model performance. For silver, we use all sentence examples in PropBank (LDC2004T14). From SQuAD2.0-C(ontexts)[1] we filter out the  $\sim 92\mathrm{K}$  sentences, removing bad utf8 encoding ( $\sim 7\mathrm{K}$ ) and ill-formed disconnected graphs produced by APT ( $\sim 15\mathrm{K}$ ). Silver comprises Ontonotes[5.0] (LDC2013T19) and silver[3] WikiText-103[12]
The results are shown in Table 3. The lower part of the table (denoted by Ours) compares the performances of Structured-BART in various silver data augmentation setups including our proposed MBSE distillation. With the same unlabeled corpus silver $^{1}$ , greedy-select distillation improves 1.0 Smatch point on AMR2.0 (84.2 vs. 85.2) and 1.5 Smatch point on AMR3.0 (82.0 vs. 83.5) over the
$^{11}$ https://rajpurkar.github.io/SQuAD-explorer/  
 $^{12}$ https://www.salesforce.com/  
products/einstein/ai-research/  
the-wikitext-dependency-language-modeling-dataset/
<sup>9</sup>https://github.com/jflanigan/jamr
10We also applied the technique to APT, observing similar performance gains when using MBSE distillation.
Models	silver	AMR2.0	AMR3.0
Naseem et al. (2019)	75.5	-
Zhang et al. (2019a)	76.3±0.1	-
Zhang et al. (2019b)	77.0±0.1	-
Cai and Lam (2020)	80.2	-
Fernandez Astudillo et al. (2020)	80.2±0.0	-
Lyu et al. (2020)	-	75.8
Lee et al. (2020)	85K	81.3±0.0	-
Xu et al. (2020)	14M	81.4	-
Bevilacqua et al. (2021)	200K	84.5	83.0
Zhou et al. (2021a)	70K	82.6±0.1	80.3
Xia et al. (2021)	1.8M	84.2	-
Bai et al. (2022)	200K	85.4	84.2
Zhou et al. (2021b)	sep-voc	joint-voc	sep-voc	joint-voc
Structured-BART-baseline	84.0±0.1	84.2±0.1	82.3±0.0	82.0±0.0
+ self-trained silver1	90K	-	84.7±0.1	82.7±0.1	82.6±0.0
+ self-trained silver1 + ensemble dec.	90K	-	84.9	83.1	-
Ours below (Struct-BART)	sep-voc	joint-voc	sep-voc	joint-voc
+ SPRING silver1	90K	84.8±0.1	84.8±0.0	83.0±0.0	83.2±0.1
+ SPRING + self-trained silver1 (50:50)	90K	84.8±0.1	84.7±0.0	83.0±0.0	83.2±0.1
Ensemble-4 distillation (APT + Structured-BART + SPRING1 + SPRING2)
+ MBSE-P silver1	90K	85.1±0.1	85.1±0.1	83.2±0.1	83.5±0.1
+ MBSE-G silver1	90K	85.0±0.0	85.2±0.1	83.4±0.0	83.5±0.0
+ MBSE-G silver1+2	149K	85.3±0.1	85.4±0.1	83.6±0.1	83.7±0.1
+ MBSE-G silver1+2+3	219K	85.3±0.1	85.5±0.1	83.7±0.0	83.9±0.0
+ MBSE-G silver1+2+3 + ensemble dec.	219K	85.6	85.7	84.0	84.2
Ensemble-5 distillation (APT + Structured-BART + SPRING1 + SPRING2 + AMRBART)
+ MBSE-A silver1	90K	85.3±0.1	83.6±0.1
+ MBSE-A silver1+2	149K	85.5±0.0	84.0±0.0
+ MBSE-A silver1+2+3	219K	85.7±0.0	84.1±0.0
+ MBSE-A silver1+2+3 + ensemble dec.	219K	85.9	84.3
Table 3: Smatch scores on AMR2.0 and AMR3.0 test data. Upper rows display the performances of recent published works. Structured-BART results in (Zhou et al., 2021b) are shown in middle rows. Lower rows show Structured-BART performances with various silver data augmentations. sep-voc denotes separate vocabulary and joint-voc, joint vocabulary. The numbers prefixed by  $\pm$  indicate the standard deviation of Smatch scores across 3 seeds.
Structured-BART baselines. Graphene-Smatch distillation performs similarly to greedy-select one.
To isolate the effect of ensembling, we provide two additional baselines: 1) silver obtained from SPRING, which is expected to have complementary information to self-trained silver, and 2) an equal mixture of SPRING and Structured-BART (random 50:50), which tests if the MBSE selection strategy bears any effect. MBSE distillation outperforms these two baselines by between 0.2 and 0.5 Smatch point, depending on the scenario, proving that MBSE selection has a clear positive effect.
We also investigate the impact of unlabeled corpus size on model performance by adding silver<sup>2</sup>
and silver $^3$  to silver $^1$ , i.e. silver $^{1+2}$  and silver $^{1+2+3}$ . We observe additional 0.3-0.4 improvement, complementary to the one obtainable with conventional ensemble decoding. This pushes the numbers to 85.7 and 84.2, setting a new SoTA for single system with 4 model ensemble (Ensemble-4) distillation. Note that using 5 model ensemble (Ensemble-5) distillation moves the Smatch scores even higher to 85.9 for AMR2.0 and 84.3 for AMR3.0.
5.2 Cross-lingual AMR Parsing
For cross-lingual AMR parsing, we consider the well known cross-lingual extension of AMR2.0 (Damonte and Cohen, 2018). Our cross-lingual