SlowGuess/NAACL_2022_OCR huggingface dataset:
Model	bram	bbj	froxx	mos	wol	hau	ibo	lug	luo	en-xx
ewe	fon	mos	pcm	swa	tsn	twi	yor	zul	AVG	MED
BLEU
M2M-100 0-shot	-	-	-	-	-	1.3	0.4	2.8	-	-	-	20.1	1.1	-	2.1	5.6	-
MT5	1.5	0.4	2.2	1.6	0.1	0.9	2.8	18.0	3.0	3.1	34.1	25.1	3.4	1.7	4.8	11.7	7.2	2.9
AfriMT5	2.1	0.8	3.7	2.5	0.1	1.8	5.1	19.6	5.2	4.6	35.0	26.7	7.0	2.7	6.2	13.2	8.5	4.8
ByT5	9.5	1.8	5.5	3.8	0.1	6.0	8.3	21.8	12.1	8.4	30.1	24.4	14.7	6.0	7.5	14.0	10.9	8.4
AfriByT5	11.4	2.2	5.2	3.7	0.2	6.4	9.3	22.7	13.1	8.9	30.0	24.7	17.0	6.1	7.6	15.3	11.5	9.1
mBART50	18.6	2.4	5.3	6.2	0.8	9.7	8.9	21.1	12.0	10.0	34.1	25.8	16.8	7.5	10.0	21.2	13.2	10.0
AfriMBART	15.3	2.4	5.7	4.4	0.6	8.6	10.4	22.4	10.0	9.8	30.0	22.7	12.8	6.3	9.6	20.1	11.9	9.9
M2M-100	22.7	2.9	6.4	7.1	1.0	12.4	16.0	24.7	14.3	11.5	33.9	26.7	24.7	8.8	12.8	21.0	15.4	13.6
M2M-100-EN/FR	18.5	2.2	6.2	4.3	0.8	10.6	7.0	22.4	8.9	9.5	34.9	26.4	19.7	7.0	5.6	15.6	12.5	9.2
CHRF
M2M-100 0-shot	-	-	-	-	-	4.3	12.4	19.0	-	-	-	47.7	8.7	-	10.4	20.1	-
MT5	10.0	7.4	9.7	11.5	7.9	9.1	23.6	41.1	24.9	21.6	64.1	53.7	22.8	17.8	20.8	36.0	23.9	21.2
AfriMT5	14.0	12.7	16.6	14.8	8.2	13.8	29.7	43.1	30.4	25.7	64.7	55.1	31.5	21.5	24.3	40.3	27.9	25.0
ByT5	27.8	17.7	23.8	16.1	8.8	22.9	31.3	46.5	40.0	32.2	58.1	52.5	38.6	27.9	25.5	40.3	31.9	29.6
AfriByT5	31.4	19.9	24.1	16.5	9.8	23.8	32.8	47.4	42.2	33.6	58.0	52.8	42.1	29.0	26.0	42.9	33.3	32.1
mBART50	42.3	22.0	27.7	25.7	16.0	31.9	32.6	45.9	41.1	36.7	64.2	54.4	43.0	35.6	31.1	50.2	37.5	36.2
AfriMBART	40.4	20.1	26.9	24.1	15.1	30.9	40.3	47.4	38.6	36.7	54.9	52.7	40.3	34.2	31.1	49.3	36.4	37.7
M2M-100	48.2	23.1	30.9	27.6	16.7	35.7	43.3	50.0	45.5	39.0	64.0	56.4	52.0	38.2	35.9	51.2	41.1	41.2
M2M-100-EN/FR	43.4	20.6	29.4	23.2	16.3	32.8	33.3	46.9	38.8	36.5	64.5	55.4	47.1	33.6	25.3	42.9	36.9	35.0
Table 3: Results adding African Languages to Pre-Trained Models, en/fr-xx. We calculate BLEU and CHRF on the news domain when training on only NEWS data from MAFAND-MT.
Zero-Shot Translation. Table 3 and Table 4 gives the result of zero-shot evaluation on NEWS. We evaluate only on the M2M-100 dataset because it has been pre-trained on parallel texts with a few of our focus languages. We observe very poor performance (< 5 BLEU) on the languages except for zul (> 13 BLEU) and swa (> 20 BLEU) in both translation directions. For swa, its likely that the performance is reasonable because M2M-100 has seen more bitext during pre-training (2.4M sentences in CCAIigned (El-Kishky et al., 2020)). Other African languages except for Afrikaans have less than 600K sentences in CCAIigned, and are also of a lower quality (Kreutzer et al., 2021) which affect overall zero-shot performance.
Performance after Fine-tuning. We found impressive performance after fine-tuning PLMs and M2M-100 on few thousand sentences (mostly 2K-7K sentences, except for swa with 30K sentences), including languages not seen during pre-training. For en/fr-xx, MT5 has a poor transfer performance with average BLEU of 7.2, despite being pretrained on 101 languages. ByT5 outperforms MT5 by over 3 BLEU on average, even though their performances were reported to be similar in previous work (Xue et al., 2021a). This indicates that ByT5 might be preferable over MT5 when translating low-resource languages. Surprisingly, mBART50 that was only pre-trained on 50 languages and 2 African languages outperformed MT5 and ByT5 which are pre-trained on 101 languages. Overall, we found M2M-100 to be the best model, most
likely because it was pre-trained on a translation task. In general, BLEU scores are relatively low ( $< 15$  BLEU for 9 out of 16 languages for en/fr-xx and 7 in xx-en/fr) even when fine-tuning M2M-100 on in-domain data, which suggests that developing more effective methods for fine-tuning might be a promising future direction. The languages with the best quality according to BLEU on the target side are pcm, swa and tsn, and pcm, zul, and swa on the source side.
BLEU scores are higher when translating from an African language, which is expected due to the more frequent exposure to English and French on the target side during pre-training, and BLEU being penalized more for morphologically rich languages like bbj, lug, swa, tsn, and zul). The ChrF metric works better for them. For example, finetuning M2M-100 on NEWS and evaluating on zul has a BLEU of 21.0 in en/fr-xx, and BLEU of 37.8 in the xx-en/fr showing a large gap in performance in both directions. However, with the ChrF, we find a smaller performance gap (51.2 in en/fr-xx and 55.5 in the xx-en/fr.
Continual Pre-training. We observe an improvement in BLEU when we utilize AfriMT5 and AfriByT5, for languages included in our continual pre-training corpus (Appendix C). Other languages also benefit despite not being seen during continual pre-training, possibly due to language similarity. For example, AfriByT5 on  $fr$ -bam improved by 1.9 BLEU over ByT5 and AfriMT5 on en-tsn improved by 3.6 BLEU over MT5. On average, AfriMT5 im
Model	bam	bbj	xx-fr	mos	wol	hau	ibo	lug	luo	xx-en
ewe	fon	mos	pcm	swa	tsn	twi	yor	zul	AVG	MED
BLEU
M2M-100 0-shot	-	-	-	-	-	0.8	2.2	6.4	-	-	-	25.2	3.3	-	3.0	13.8	-	-	-
MT5	2.5	0.9	1.1	2.4	0.7	1.3	5.8	18.9	12.6	6.4	42.2	29.5	9.5	4.6	12.3	22.4	10.8	6.1
AfriMT5	6.4	2.0	2.1	4.2	1.2	2.9	10.4	19.5	15.5	9.7	44.6	30.6	16.1	8.4	13.8	24.0	13.2	10.0
ByT5	10.0	2.7	4.1	4.9	1.5	7.2	12.9	21.0	19.8	12.1	39.4	27.1	18.6	9.8	11.5	22.8	14.1	11.8
AfriByT5	13.8	4.4	4.5	5.8	2.2	9.0	13.5	20.7	21.1	12.5	39.5	27.0	19.7	10.5	11.9	24.0	15.0	13.0
mBART50	6.8	0.3	1.7	0.8	0.6	6.3	11.5	13.2	14.5	9.1	44.2	29.0	2.0	0.5	8.1	31.1	11.2	7.4
AfriMBART	8.1	2.3	3.0	4.5	1.7	3.2	10.2	15.5	13.1	8.0	43.7	29.2	7.2	6.5	9.5	33.0	12.4	8.0
M2M-100	22.1	5.4	6.9	8.4	2.8	10.3	17.0	19.0	20.0	13.0	43.8	29.8	20.0	10.9	16.0	37.8	17.7	16.5
M2M-100-EN/FR	22.1	5.1	7.4	9.1	2.1	10.5	11.4	20.3	19.8	14.0	45.2	30.0	21.4	11.7	13.4	9.5	15.8	12.6
CHRF
M2M-100 0-shot	-	-	-	-	-	12.3	23.7	29.7	-	-	-	51.6	21.1	-	18.3	35.7	-	-
MT5	19.4	15.1	17.0	17.9	10.9	16.2	26.3	43.5	36.3	26.1	66.9	53.7	32.2	25.2	31.1	43.9	30.1	26.2
AfriMT5	27.7	19.6	21.1	21.4	13.2	21.6	32.5	44.9	40.2	32.2	68.4	54.5	39.6	31.2	33.9	45.9	34.2	32.4
ByT5	31.2	21.8	24.8	20.5	15.4	26.2	33.2	46.4	45.4	34.1	62.0	50.6	42.4	32.9	31.4	42.5	35.0	33.0
AfriByT5	34.8	25.5	24.9	22.0	16.2	29.3	33.9	46.4	47.1	35.0	62.1	50.5	43.4	33.4	32.0	43.7	36.3	34.3
mBART50	26.0	17.1	20.9	20.2	17.1	26.6	32.0	37.9	39.0	31.0	68.2	53.5	20.1	19.4	26.7	49.0	31.5	26.6
AfriMBART	31.4	22.9	27.2	26.3	17.0	25.0	34.3	42.0	40.4	29.8	67.8	53.5	31.4	30.6	30.0	51.7	35.1	31.0
M2M-100	45.9	26.5	30.9	27.5	17.7	33.8	38.7	46.1	46.4	36.7	68.6	54.8	45.2	35.1	38.1	55.5	40.5	38.4
M2M-100-EN/FR	45.6	26.9	32.2	28.7	17.0	34.3	35.1	46.6	46.0	37.6	69.0	55.0	46.3	36.0	35.2	31.5	38.9	35.6
Table 4: Results adding African Languages to Pre-Trained Models, xx-en/fr. We calculate BLEU and CHRF on the news domain when training on only NEWS data from MAFAND-MT.
proved over MT5 by 1.3 BLEU in en/fr-xx and 2.4 BLEU in the xx-en/fr. The improvement for AfriByT5 was much smaller: 0.6 and 0.9 BLEU in en/fr-xx and xx-en/fr translation directions. For AfriMBART, we did not see any improvement on average, only the performance of hau (1.5 BLEU) and ibo (0.7 BLEU) improved in en/fr-xx direction. However, in the xx-en/fr direction, fon, tsn, twi, and zul improved by 2.7-6.0 BLEU.
Many-to-Many Multilingual MT. Training on the combined news corpus from all languages that use French or English separately does not appear to help much. We see slight improvements for most languages only in the  $xx$ -en/fr direction.
6.2 Adaptation to the News Domain
To improve over the baseline performance on NEWS, we train bilingual Transformer models (as a baseline) and M2M-100 on a combination of REL and NEWS. We chose M2M-100 because it was the best performing model. Table 5 gives the BLEU on three settings: REL+NEWS, REL  $\rightarrow$  NEWS, and REL+NEWS  $\rightarrow$  NEWS. In general, the improvement depends on the size of REL corpus. For languages trained on the Bible such as bbj, bam, lug, luo, and wol, the improvement is minimal. For M2M-100, the REL+NEWS performance does not improve over NEWS despite the larger quantity of training data. This demonstrates that increasing the size in the target domain is the most helpful strategy (see Figure 2). Similarly, combining REL+NEWS
Figure 1: Domain shift of M2M-100 Transformer models trained on en/fr-xx (top) or xx-en/fr (bottom) REL domain and tested on the NEWS vs. REL domains.
is not very helpful for xx-en/fr. An alternative approach is  $\mathrm{REL} \rightarrow \mathrm{NEWS}$ , which allows the model to develop a good understanding of the desired language before adapting to the news domain. We observe an increase on 1.1 BLEU over  $\mathrm{REL} + \mathrm{NEWS}$  in the en/fr-xx direction. However, the best strategy is  $\mathrm{REL} + \mathrm{NEWS} \rightarrow \mathrm{NEWS}$ , especially for xx-en/fr where it yields an improvement over NEWS and  $\mathrm{REL} + \mathrm{NEWS}$  by 2.0 and 1.5 BLEU, respectively.
6.3 Analysis of Domain Shift
Is a small in-domain set essential for finetuning? If we train models only on previously available religious data, they are not capable of translating news well due to the strong domain bias. This is illustrated in Figure 1: All models perform much worse on NEWS than on the REL do
Model	\(f r-xx\)	mos	wol	hau	ibo	lug	luo	en-xx
bam	bbj	ewe	fon	pcm	swa	tsn	twi	yor	zul	AVG	MED
BLEU
Transformer
REL+NEWS	7.3	0.1	6.2	2.9	2.1	3.1	10.7	22.4	4.6	3.7	11.7	26.2	28.1	8.7	9.7	16.5	10.2	8.0
REL→NEWS	5.1	0.2	5.4	2.8	1.7	2.3	11.7	22.7	3.9	3.3	11.9	26.3	29.7	8.7	8.4	20.3	10.3	6.9
REL+NEWS→NEWS	8.5	0.3	6.5	3.2	2.2	3.7	12.0	23.6	5.1	4.3	13.8	26.6	29.3	9.0	9.7	20.1	11.1	8.8
M2M-100
REL+NEWS	23.0	2.8	7.7	6.5	0.9	11.2	12.9	24.7	13.9	11.6	35.1	23.3	29.0	9.7	12.4	18.3	15.2	12.6
REL→NEWS	20.3	3.1	7.7	7.5	1.1	12.0	15.0	26.0	15.4	11.9	35.0	27.7	31.9	10.0	13.4	22.9	16.3	14.2
REL+NEWS→NEWS	24.7	3.1	8.9	7.4	1.1	12.7	15.9	25.8	15.7	12.0	34.2	27.3	31.9	10.2	13.9	22.6	16.7	14.8
CHRF
Transformer
REL+NEWS	25.6	9.6	30.6	14.5	17.7	18.9	36.7	46.7	30.5	26.4	37.8	55.3	55.0	36.7	30.6	50.0	32.7	30.6
REL→NEWS	18.2	11.2	27.1	15.4	18.3	15.9	37.4	47.2	28.7	24.4	38.3	55.5	56.3	36.6	28.9	53.0	32.0	28.8
REL+NEWS→NEWS	27.4	12.8	31.5	16.5	19.9	20.2	38.3	48.3	30.6	27.7	42.6	55.6	56.3	37.7	30.6	53.4	34.3	31.0
M2M-100
REL+NEWS	46.8	22.1	36.7	26.2	16.0	33.5	38.4	50.1	44.5	38.1	64.7	53.0	57.2	39.7	35.2	53.1	41.0	39.0
REL→NEWS	44.1	22.6	34.1	27.7	16.8	34.7	41.3	51.3	45.6	38.6	64.7	57.2	59.3	40.6	37.1	56.3	42.0	41.0
REL+NEWS→NEWS	49.9	23.5	37.5	28.5	16.8	35.8	42.1	51.3	46.9	39.4	64.2	57.0	59.5	40.8	37.4	56.3	42.9	41.4
Table 5: Results adapting to Domain Shift, en/fr-xx. We calculate BLEU and ChrF on the news domain when training on different combinations of REL and NEWS.
Model	xx-fr	xx-en
bam	bbj	ewe	fon	mos	wol	hau	ibo	lug	luo	pcm	swa	tsn	twi	yor	zul	AVG	MED
BLEU
Transformer
REL+NEWS	4.9	0.6	6.3	2.2	3.7	2.2	11.2	17.4	5.6	3.1	19.5	28.0	23.9	9.8	12.0	27.3	11.1	8.0
REL→NEWS	4.7	0.8	6.5	2.4	3.1	2.5	11.0	17.4	6.3	1.8	19.0	27.9	24.6	10.1	11.0	28.5	11.1	8.3
REL+NEWS→NEWS	5.8	1.0	7.1	2.4	4.1	2.6	13.2	18.2	6.8	3.7	21.4	28.7	24.5	10.4	12.6	30.1	12.0	8.8
M2M-100
REL+NEWS	24.0	5.8	10.9	9.7	2.3	10.1	15.3	21.1	21.1	13.3	44.6	29.4	27.0	12.5	17.4	30.6	18.4	16.4
REL→NEWS	20.3	5.9	11.4	9.6	2.3	10.5	17.4	21.9	20.6	13.7	44.3	30.6	27.7	13.2	18.0	36.0	19.0	17.7
REL+NEWS→NEWS	25.8	6.3	11.6	9.9	2.6	11.5	18.2	21.5	22.4	14.3	44.0	30.5	27.8	13.2	18.0	38.1	19.7	18.1
CHRF
Transformer
REL+NEWS	24.7	12.6	29.4	16.1	17.6	19.9	31.7	43.1	26.9	23.0	47.8	53.5	49.8	34.4	33.4	49.6	32.1	30.6
REL→NEWS	23.0	12.7	29.8	16.6	17.2	18.3	30.6	42.8	28.7	20.0	47.3	53.3	50.8	34.4	32.2	50.4	31.8	30.2
REL+NEWS→NEWS	26.5	14.7	30.7	17.6	18.8	21.8	33.8	44.0	29.5	24.7	50.8	54.1	50.6	35.1	34.4	51.4	33.7	32.2
M2M-100
REL+NEWS	47.1	27.5	36.4	27.9	16.6	34.0	36.8	47.5	47.2	37.3	68.9	54.7	53.0	38.4	40.2	53.3	41.7	39.3
REL→NEWS	44.5	27.7	37.0	28.2	16.8	34.4	39.6	48.0	47.0	38.0	68.7	55.8	53.6	38.7	40.7	56.4	42.2	40.2
REL+NEWS→NEWS	49.0	28.5	37.2	28.9	17.2	35.3	40.2	47.9	48.5	38.3	68.6	55.7	54.0	38.7	41.0	57.7	42.9	40.6
Table 6: Results adapting to Domain Shift, xx-en/fr. We calculate BLEU and ChrF on the news domain when training on different combinations of REL and NEWS.
bam-fr
SRC	Ani k’a fou ye ko cemance fanga be sigi ntuloma saban kan.
TGT	Et leur dire que la transition se repose sur trois piliers.
REL	Et qu'on leur dire que la puissance du milieu est sur trois sauterelles;
R+N→N	Et de leur dire que la force de la transition repose sur trois piliers.
lug-en
SRC	Murasaki Shikibu yawandiika ekitabo ekijuvu akaasookera ddala mu ni si yonna.
TGT	Murasaki Shikibu wrote the world’s first full novel.
REL	And Murshach Shikib writes a full scroll of the first in all the earth.
R+N→N	Murasaki Shikibu wrote a complete book first in the world.
Table 7: Example translations for M2M-100 finetuned on REL or REL+NEWS→NEWS (R+N→N). Terms in red are typical for biblical texts, while the terms in blue are more neutral expressions.
main. When the quantity of religious training data is small, the loss in translation performance on the news test set is largest, c.f. bbj (8k of REL data) with a drop of  $-95.5\%$  BLEU or bam  $(-93.5\%, 28k)$  and luo  $(-93.5\%, 31k)$ . This indicates that when
the REL training data is sparse, it is insufficient to teach the M2M-100 model a more general understanding required for translating NEWS. However, when the religious training data is larger, this loss is reduced, c.f. when translating to zul (667k, - 67%), swa (-69.3%, 872k), and tsn (-71%, 870k). While this is the general trend, pcm, whose religious training data is small (23k), has the lowest drop in performance (-59.3%), which may be due to the strong similarity to its source language.
How many sentences in the target domain are required? Figure 2 shows how for three selected language pairs with a large (fr-bam), medium (eng-ibo) and relatively small (eng-swa) domain gap, the quality of target domain translations improves as we increase the size of the target domain corpus. For all three pairs, fine-tuning M2M
(a) eng-swa
(b) eng-ibo
Figure 2: Number of fine-tuning sentences needed to exceed the performance of a bilingual Transformer model.
(c) fr-bam
Evaluation Domain	Tuned on NEWS	hau	ibo	lug	luo	swa	wol	yor	zul
en/fr-xx
FLORES	✘	2.6	2.8	0.8	-	20.9	0.6	1.5	3.3
FLORES	✓	4.0	19.9	7.6	13.7	27.1	8.2	13.4	19.2
REL	✘	1.2	1.0	0.0	-	11.0	0.0	0.4	1.6
REL	✓	3.7	10.3	3.3	5.4	14.6	6.7	10.6	13.0
xx-en/fr
FLORES	✘	8.0	7.2	3.7	-	26.9	3.0	3.8	11.9
FLORES	✓	16.3	12.0	7.7	11.8	25.8	7.5	9.3	19.2
REL	✘	6.4	3.7	0.5	-	15.4	0.4	0.9	8.5
REL	✓	3.8	6.0	1.7	2.5	13.9	1.7	5.7	12.5
Table 8: spBLEU on Wikipedia domain (FLORES) and REL for M2M-100 before  $(\mathcal{X})$  and after  $(\checkmark)$  fine-tuning on NEWS.
100 or ByT5 on  $2.5k$  sentence pairs of in-domain data (NEWS) is sufficient to outperform the bilingual Transformer baselines that were additionally trained on larger amounts of out-of-domain data (REL). Surprisingly, this procedure not only works for languages included during pre-training (swa), but also for previously unseen languages (ibo, bam). M2M-100 tends to adapt to the new data more quickly than ByT5, but in all cases, models continue to learn with additional in-domain data. This shows how much more effectively a small number of in-domain translations can be used when they serve for fine-tuning multilingual pre-trained models rather than training bilingual MT models from scratch.
Examples of Domain Bias. To illustrate the challenge of overcoming domain bias, we show examples translating from bam and lug in Table 7. The M2M-100 model fine-tuned only on REL succeeds in roughly capturing the meaning of the sources, but using biblical terms, such as "scroll" instead of "novel". Adding our news corpus to fine-tuning resolves these issues (e.g. "book").
How general is our news corpus? Table 8 shows the zero-shot evaluation of M2M-100 fine-tuned on our small NEWS corpora on other domains: reli
gious (REL) and Wikipedia (FLORES). We evaluated the Wikipedia domain on the FLORES devtest and the REL domain on either JW300 or Bible (lug, luo, wol). As a baseline, we evaluated the zero-shot performance of M2M-100 (not finetuned,  $\pmb{x}$ ) on FLORES $^{10}$  using spBLEU (i.e. sentencepiece BLEU (Goyal et al., 2021)). We noticed very poor performance except for Swahili — as discussed in §6.1. After fine-tuning on our new data ( $\checkmark$ ), transfer is largely improved across the bench (up to +17 BLEU for en-ibo). The same trend holds for the religious domain. This shows that even though our data comes from the news domain, it helped the model generalize to other domains. Hence, expanding African news corpora and developing better MT models for news pays off even for other domains of interest.
We have created MAFAND-MT, a corpus of 16 African languages to study translation systems for low-resource languages in the news domain. We investigate how to most effectively adapt large-scale pre-trained models to incorporate new languages and new domains. Our findings suggest that as little as 2k sentences are sufficient for fine-tuning, with an improved performance, paving the way for others to create new translation systems without relying on large collections of web-sourced text. This has strong implications for languages that are spoken by millions but lack presence on the web.
In the future, we hope to expand our coverage to additional under-resourced languages, and to develop even more effective fine-tuning objectives. Currently, we are extending our corpus to Chichewa, Kinyarwanda, Shona, and isiXhosa, including an expansion of the Hausa corpus, they will be released under MAFAND-MT dataset name.
10 except for Luo which is not supported
This work was carried out with support from Lacuna Fund, an initiative co-founded by The Rockefeller Foundation, Google.org, and Canada's International Development Research Centre. David Adelani acknowledges the EU-funded Horizon 2020 projects: COMPRISE (http://www.compriseh2020.eu/) under grant agreement No. 3081705 and ROXANNE under grant number 833635. We thank Chester Chester Palen-Michel and Constantine Lignos for providing the VOA corpus for this research, and Google for providing GCP credits to run some of the experiments. Finally, we thank Davor Orlic and Knowledge4All for their administrative support throughout the project.
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