SlowGuess/NAACL_2021_OCR huggingface dataset:
Predicate sense disambiguation. In Table 9 we report the results of our model on predicate sense disambiguation.
F Alignment Examples
Figure 3 provides two more examples, one in French (left), the other in Catalan (right). We remark that the training set of CoNLL-2009 does not include sentences in French, however, our crosslingual model correctly outputs SRL tags according to the other seven language-specific decoders.
<sup>8</sup>https://github.com/NVIDIA/apex
Sentences	Predicates	Arguments
TotalS	Annotated	Avg. Len.	Totalp	Senses	Totala	Roles
CoNLL-2009
CA	13,200	12,873	30.2	37,431	3,554	84,367	38
CZ	38,727	38,578	16.9	414,237	9,135	365,255	60
DE	36,020	14,282	22.2	17,400	1,271	34,276	10
EN	39,279	37,847	25.0	179,014	8,237	393,699	52
ES	14,329	13,835	30.7	43,824	4,534	99,054	43
ZH	22,277	21,071	28.5	102,813	12,587	231,869	36
Table 5: Overview of the CoNLL-2009 training sets. For each dataset we report the number of sentences  $(Total_{s})$ , the number of sentences with at least an annotated predicate (Annotated), the average number of tokens per sentence (Avg. Len.), the number of predicates  $(Total_{p})$  and predicate senses (Senses), and also the number of arguments  $(Total_{a})$  and argument roles (Roles).
Sentences	Predicates	Arguments
Totals	Annotated	Avg. Len.	Totalp	Senses	Totala	Roles
CoNLL-2009
CA	1,724	1,675	31.5	5,105	1,436	11,529	34
CZ	5,228	5,210	16.9	55,517	3,467	49,071	54
DE	2,000	532	19.7	588	255	1,169	9
EN	1,334	1,283	25.7	6,390	1,990	13,865	32
ES	1,655	1,588	31.4	5,076	1,565	11,600	36
ZH	1,762	1,663	29.5	8,103	2,535	18,554	24
Table 6: Overview of the CoNLL-2009 development datasets. For each dataset we report the number of sentences  $(Total_{s})$ , the number of sentences with at least an annotated predicate (Annotated), the average number of tokens per sentence (Avg. Len.), the number of predicates  $(Total_{p})$  and predicate senses (Senses), and also the number of arguments  $(Total_{a})$  and argument roles (Roles).
Sentences	Predicates	Arguments
Totals	Annotated	Avg. Len.	Totalp	Senses	Totala	Roles
CoNLL-2009
CA	1,862	1,802	29.4	5,001	1,425	11,275	32
CZ	4,213	4,196	16.8	44,585	3,018	39,223	55
DE	2,000	506	20.1	550	238	1,073	8
EN	2,000	1,913	25.0	8,987	2,254	19,949	35
ES	1,725	1,663	30.2	5,175	1,623	11,824	33
ZH	2,556	2,400	30.1	12,282	3,458	27,712	26
Table 7: Overview of the CoNLL-2009 testing datasets. For each dataset we report the number of sentences  $(Total_{s})$ , the number of sentences with at least an annotated predicate (Annotated), the average number of tokens per sentence (Avg. Len.), the number of predicates  $(Total_{p})$  and predicate senses (Senses), and also the number of arguments  $(Total_{a})$  and argument roles (Roles).
CONLL-2009 - PREDICATE IDENTIFICATION	CA	CZ	DE	EN	ES	ZH
This work m-BERT frozen / monolingual	97.9	98.6	90.5	93.8	97.8	94.3
This work m-BERT / monolingual	98.3	98.9	91.4	94.3	98.4	95.0
This work m-BERT / cross-lingual	98.3	99.0	91.6	94.4	98.4	95.1
This work XLM-R frozen / monolingual	97.9	98.9	90.5	93.9	98.0	94.7
This work XLM-R / monolingual	98.3	99.2	91.5	94.3	98.4	95.2
This work XLM-R / cross-lingual	98.5	99.3	91.9	94.6	98.6	95.4
Table 8:  ${\mathrm{F}}_{1}$  scores on the predicate identification subtask which is not part of the CoNLL-2009 shared task setting.
CONLL-2009 - PREDICATE DISAMBIGUATION	CA	CZ	DE	EN	ES	ZH
This work m-BERT frozen / monolingual	90.0	93.2	86.9	96.8	87.3	94.9
This work m-BERT / monolingual	90.3	93.5	87.3	97.2	87.5	95.0
This work m-BERT / cross-lingual	90.3	93.5	87.3	97.2	87.6	95.3
This work XLM-R frozen / monolingual	90.1	93.6	86.8	96.8	87.4	95.2
This work XLM-R / monolingual	90.4	93.7	87.3	97.1	87.6	95.6
This work XLM-R / cross-lingual	90.5	93.9	87.5	97.2	87.8	95.8
Table 9: Accuracy on the predicate sense disambiguation subtask computed by the official CoNLL-2009 scorer which, by default, takes into account only the sense numbers, e.g., 01 of eat.01.
Figure 3: Output of our cross-lingual system for a French (left) and a Catalan (right) sentence.