SlowGuess/ACL_2020_OCR huggingface dataset:
Table 5: Scores from UDPipe 2.0 (from Kondratyuk and Straka, 2019), the previous state-of-the-art models UDPipe  $2.0 + \mathrm{mBERT}$  (Straka et al., 2019) and UDify (Kondratyuk and Straka, 2019), and our ELMo-enhanced UDPipe 2.0 models. Test scores are given for UPOS, UAS and LAS in all five languages. Best scores are shown in bold, second best scores are underlined.
of overfitting, as the UDPipe  $2.0 + \mathrm{ELMo_{Wikipedia}}$  results considerably improve the UDPipe 2.0 baseline. This is the case for all of our ELMoWikipedia models as we never see any evidence of a negative impact when we add them to the baseline model. In fact, the results of UDPipe  $2.0 + \mathrm{ELMo_{Wikipedia}}$  give better than previous state-of-the-art results in all metrics for the Finnish-FTB and in UPOS for the Finnish-TDT. The results for Finnish are actually quite interesting, as mBERT was pre-trained on Wikipedia and here we see that the multilingual setting in which UDify was fine-tuned exhibits subbaseline results for all metrics, and that the UDPipe  $^+$  mBERT scores are often lower than those of our UDPipe  $2.0 + \mathrm{ELMo_{Wikipedia}}$ . This actually suggests that even though the multilingual approach of mBERT (in pre-training) or UDify (in pre-training and fine-tuning) leads to better performance for high-resource languages or languages
15http://scripta.kotus.fi/visk
Treebank	Model	UPOS	UAS	LAS
Bulgarian BTB	UDPipe 2.0	98.98	93.38	90.35
+ELMowikipedia(1)	98.81	93.60	90.21
+ELMowikipedia(3)	99.01	94.32	91.36
+ELMowikipedia(5)	99.03	94.32	91.38
+ELMowikipedia(10)	99.17	94.93	92.05
+ELMowOSCAR(1)	99.28	95.45	92.98
+ELMowOSCAR(3)	99.34	95.58	93.12
+ELMowOSCAR(5)	99.34	95.63	93.25
+ELMowOSCAR(10)	99.40	96.01	93.56
Catalan-AnCora	UDPipe 2.0	98.88	93.22	91.06
+ELMowikipedia(1)	98.93	93.24	91.21
+ELMowikipedia(3)	99.02	93.75	91.93
+ELMowikipedia(5)	99.04	93.86	92.05
+ELMowikipedia(10)	99.05	93.99	92.24
+ELMowOSCAR(1)	99.07	93.92	92.29
+ELMowOSCAR(3)	99.10	94.29	92.69
+ELMowOSCAR(5)	99.07	94.38	92.75
+ELMowOSCAR(10)	99.06	94.49	92.88
Danish-DDT	UDPipe 2.0	97.78	86.88	84.31
+ELMowikipedia(1)	97.47	86.98	84.15
+ELMowikipedia(3)	98.03	88.16	85.81
+ELMowikipedia(5)	98.15	88.24	85.96
+ELMowikipedia(10)	98.45	89.05	86.92
+ELMowOSCAR(1)	98.50	89.47	87.43
+ELMowOSCAR(3)	98.59	89.68	87.77
+ELMowOSCAR(5)	98.59	89.46	87.64
+ELMowOSCAR(10)	98.62	89.84	87.95
Treebank	Model	UPOS	UAS	LAS
Finnish-FTB	UDPipe 2.0	96.65	90.68	87.89
+ELMowikipedia(1)	95.86	89.63	86.39
+ELMowikipedia(3)	96.76	91.02	88.27
+ELMowikipedia(5)	96.97	91.66	89.04
+ELMowikipedia(10)	97.27	92.05	89.62
+ELMooSCAR(1)	97.91	93.41	91.43
+ELMooSCAR(3)	98.00	93.99	91.98
+ELMooSCAR(5)	98.15	93.98	92.24
+ELMooSCAR(10)	98.13	93.81	92.02
Finnish-TDT	UDPipe 2.0	97.45	89.88	87.46
+ELMowikipedia(1)	96.73	89.11	86.33
+ELMowikipedia(3)	97.55	90.84	88.50
+ELMowikipedia(5)	97.55	91.11	88.88
+ELMowikipedia(10)	97.65	91.60	89.34
+ELMooSCAR(1)	98.27	93.03	91.29
+ELMooSCAR(3)	98.38	93.60	91.83
+ELMooSCAR(5)	98.39	93.57	91.80
+ELMooSCAR(10)	98.36	93.54	91.77
Indonesian-GSD	UDPipe 2.0	93.69	85.31	78.99
+ELMowikipedia(1)	93.70	85.81	79.46
+ELMowikipedia(3)	93.90	86.04	79.72
+ELMowikipedia(5)	94.04	85.93	79.97
+ELMowikipedia(10)	93.94	86.16	80.10
+ELMooSCAR(1)	93.95	86.25	80.23
+ELMooSCAR(3)	94.00	86.21	80.14
+ELMooSCAR(5)	94.23	86.37	80.40
+ELMooSCAR(10)	94.12	86.49	80.59
Table 6: UPOS, UAS and LAS scores for the UDPipe 2.0 baseline reported by (Kondratyuk and Straka, 2019), plus the scores for checkpoints at 1, 3, 5 and 10 epochs for all the ELMoOSCAR and ELMoWikipedia. All scores are test scores. Best ELMoOSCAR scores are shown in bold while best ELMoWikipedia scores are underlined.
that are closely related to high-resource languages, it might also significantly degrade the representations for more isolated or even simply more morphologically rich languages like Finnish. In contrast, our monolingual approach with UDPipe 2.0  $+\mathrm{ELMo}_{\mathrm{OSCAR}}$  improves the previous SOTA considerably, by more than 2 points for some metrics. Note however that Indonesian, which might also be seen as a relatively isolated language, does not behave in the same way as Finnish.
5.2 Impact of the number of training epochs
An important topic we wanted to address with our experiments was that of overfitting and the number of epochs one should train the contextualized embeddings for. The ELMo authors have expressed that increasing the number of training epochs is generally better, as they argue that training the ELMo model for longer reduces held-out perplexity and further improves downstream task performance.[16] This is why we intentionally fully pre-trained the ELMoWikipedia to the 10 epochs of the original ELMo paper, as its authors also expressed concern over the possibility of overfitting for smaller corpora. We thus save checkpoints for
each of our ELMo model at the 1, 3, 5 and 10 epoch marks so that we can properly probe for overfitting. The scores of all checkpoints are reported in Table 6. Here again we do not train the UDPipe 2.0 baselines without embedding, we just report the scores published in Kondratyuk and Straka (2019).
The first striking finding is that even though all our Wikipedia data sets are smaller than 1GB in size (except for Catalan), none of the ELMoWikipedia models show any sign of overfitting, as the results continue to improve for all metrics the more we train the ELMo models, with the best results consistently being those of the fully trained 10 epoch ELMos. For all of our Wikipedia models, but those of Catalan and Indonesian, we see sub-baseline results at 1 epoch; training the model for longer is better, even if the corpora are small in size.
ELMoOSCAR models exhibit exactly the same behavior as ELMoWikipedia models where the scores continue to improve the longer they are pre-trained, except for the case of Finnish. Here we actually see an unexpected behavior where the model performance caps around the  $3^{\mathrm{rd}}$  to  $5^{\mathrm{th}}$  epoch. This is surprising because the Finnish OSCAR corpus is more than 20 times bigger than our smallest Wikipedia corpus, the Danish Wikipedia, that did not exhibit
16Their comments on the matter can be found here.
this behavior. As previously mentioned Finnish is morphologically richer than the other languages in which we trained ELMo, we hypothesize that the representation space given by the ELMo embeddings might not be sufficiently big to extract more features from the Finnish OSCAR corpus beyond the  $5^{\text{th}}$  epoch mark, however in order to test this we would need to train a larger language model like BERT which is sadly beyond our computing infrastructure limits (cf. Subsection 5.3). However we do note that pre-training our current language model architectures in a morphologically rich language like Finnish might actually better expose the limits of our existing approaches to language modeling.
One last thing that it is important to note with respect to the number of training epochs is that even though we fully pre-trained our ELMoWikipedia's and ELMoOSCAR's to the recommended 10 epoch mark, and then compared them against one another, the number of training steps between both pre-trained models differs drastically due to the big difference in corpus size (for Indonesian, for instance, 10 epochs correspond to 78K steps for ELMoWikipedia and to 2.6M steps for OSCAR; the complete picture is provided in the Appendix, in Table 8). In fact, we can see in Table 6 that all the UDPipe  $2.0 + \mathrm{ELMoOSCAR(1)}$  perform better than the UDPipe  $2.0 + \mathrm{ELMoWikipedia(1)}$  models across all metrics. Thus we believe that talking in terms of training steps as opposed to training epochs might be a more transparent way of comparing two pretrained models.
5.3 Computational cost and carbon footprint
Considering the discussion above, we believe an interesting follow-up to our experiments would be training the ELMo models for more of the languages included in the OSCAR corpus. However training ELMo is computationally costly, and one way to estimate this cost, as pointed out by Strubell et al. (2019), is by using the training times of each model to compute both power consumption and  $\mathrm{CO}_{2}$  emissions.
In our set-up we used two different machines, each one having 4 NVIDIA GeForce GTX 1080 Ti graphic cards and 128GB of RAM, the difference between the machines being that one uses a single Intel Xeon Gold 5118 processor, while the other uses two Intel Xeon E5-2630 v4 processors. One GeForce GTX 1080 Ti card is rated at around
Language	Power	Hours	Days	KWh-PUE	CO2e
OSCAR-Based ELMos
Bulgarian	1183	515.00	21.45	962.61	49.09
Catalan	1118	199.98	8.33	353.25	18.02
Danish	1183	200.89	8.58	375.49	19.15
Finnish	1118	591.25	24.63	1044.40	53.26
Indonesian	1183	694.26	28.93	1297.67	66.18
Wikipedia-Based ELMos
Bulgarian	1118	15.45	0.64	27.29	1.39
Catalan	1118	51.08	2.13	90.22	4.60
Danish	1118	14.56	0.61	25,72	1.31
Finnish	1118	21.79	0.91	38.49	1.96
Indonesian	1118	20.28	0.84	35.82	1.82
TOTAL EMISSIONS	216.78
Table 7: Average power draw (Watts), training times (in both hours and days), mean power consumption (KWh) and  $\mathrm{CO}_{2}$  emissions (kg) for each ELMo model trained.
250 W, $^{17}$  the Xeon Gold 5118 processor is rated at  $105\mathrm{W},^{18}$  while one Xeon E5-2630 v4 is rated at  $85\mathrm{W}.$ ^{19} For the DRAM we can use the work of Desrochers et al. (2016) to estimate the total power draw of 128GB of RAM at around 13W. Having this information, we can now use the formula proposed by Strubell et al. (2019) in order to compute the total power required to train one ELMo model:
$$
p _ {t} = \frac {1 . 5 8 t (c p _ {c} + p _ {r} + g p _ {g})}{1 0 0 0}
$$
Where  $c$  and  $g$  are the number of CPUs and GPUs respectively,  $p_c$  is the average power draw (in Watts) from all CPU sockets,  $p_r$  the average power draw from all DRAM sockets, and  $p_g$  the average power draw of a single GPU. We estimate the total power consumption by adding GPU, CPU and DRAM consumptions, and then multiplying by the Power Usage Effectiveness (PUE), which accounts for the additional energy required to support the compute infrastructure. We use a PUE coefficient of 1.58, the 2018 global average for data centers (Strubell et al., 2019). In table 7 we report the training times in both hours and days, as well as the total power draw (in Watts) of the system used to train each individual ELMo model. We use this in
17https://www.geforce.com/hardware/ desktop-gpus/geforce-gtx-1080-ti/ specifications 18https://ark.intel.com/content/www/ us/en/ark/products/120473/intel-xeongold-5118-processor-16-5m-cache-2-30- ghz.html 19https://ark.intel.com/content/www/ us/en/ark/products/92981/intel-xeon- processor-e5-2630-v4-25m-cache-2-20-ghz. html
formation to compute the total power consumption of each ELMo, also reported in table 7.
We can further estimate the  $\mathrm{CO}_{2}$  emissions in kilograms of each single model by multiplying the total power consumption by the average  $\mathrm{CO}_{2}$  emissions per kWh in France (where the models were trained). According to the RTE (Réseau de transport d'électricité / Electricity Transmission Network) the average emission per kWh were around  $51\mathrm{g/kWh}$  in November 2019,[20] when the models were trained. Thus the total  $\mathrm{CO}_{2}$  emissions in kg for one single model can be computed as:
$$
\mathrm {C O} _ {2} \mathrm {e} = 0. 0 5 1 p _ {t}
$$
All emissions for the ELMo models are also reported in table 7.
We do not report the power consumption or the carbon footprint of training the UDPipe 2.0 architecture, as each model took less than 4 hours to train on a machine using a single NVIDIA Tesla V100 card. Also, this machine was shared during training time, so it would be extremely difficult to accurately estimate the power consumption of these models.
Even though it would have been interesting to replicate all our experiments and computational cost estimations with state-of-the-art fine-tuning models such as BERT, XLNet, RoBERTa or ALBERT, we recall that these transformer-based architectures are extremely costly to train, as noted by the BERT authors on the official BERT GitHub repository,[21] and are currently beyond the scope of our computational infrastructure. However we believe that ELMo contextualized word embeddings remain a useful model that still provide an extremely good trade-off between performance to training cost, even setting new state-of-the-art scores in parsing and POS tagging for our five chosen languages, performing even better than the multilingual mBERT model.
In this paper, we have explored the use of the Common-Crawl-based OSCAR corpora to train ELMo contextualized embeddings for five typologically diverse mid-resource languages. We have compared them with Wikipedia-based ELMo embeddings on two classical NLP tasks, POS tagging
and parsing, using state-of-the-art neural architectures. Our goal was to explore whether the noisiness level of Commoncrawl data, often invoked to criticize the use of such data, could be compensated by its larger size; for some languages, the OSCAR corpus is several orders of magnitude larger than the corresponding Wikipedia. Firstly, we found that when properly filtered, Commoncrawl data is not massively noisier than Wikipedia. Secondly, we show that embeddings trained using OSCAR data consistently outperform Wikipedia-based embeddings, to the extent that they allow us to improve the state of the art in POS tagging and dependency parsing for all the 6 chosen treebanks. Thirdly, we observe that more training epochs generally results in better embeddings even when the training data is relatively small, as is the case for Wikipedia.
Our experiments show that Common-Crawl-based data such as the OSCAR corpus can be used to train high-quality contextualized embeddings, even for languages for which more standard textual resources lack volume or genre variety. This could result in better performances in a number of NLP tasks for many non highly resourced languages.
We want to thank Ganesh Jawahar for his insightful comments and suggestions during the early stages of this project. This work was partly funded by the French national ANR grant BASNUM (ANR-18-CE38-0003), as well as by the last author's chair in the PRAIRIE institute,[22] funded by the French national ANR as part of the "Investissements d'avirr" programme under the reference ANR-19-P3IA-0001. The authors are grateful to Inria Sophia Antipolis - Méditerranée "Nef"[23] computation cluster for providing resources and support.
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