\begin{table*}
\centering
\footnotesize
\begin{tabular}{lcccccc|c}
\toprule
& \multicolumn{2}{c}{\bf Lexical} & \multicolumn{2}{c}{\bf Entities} & \multicolumn{2}{c}{\bf Random} & \bf FRMT \\
\bf Model & pt-BR & pt-PT & pt-BR & pt-PT & pt-BR & pt-PT & pt\\\midrule
UR & 37.4 (69.9) & 32.7 (68.0) & 46.7 (76.3) & 40.8 (73.6) & 39.8 (70.7) & 35.3 (69.2) & 38.7 (71.3)\\
M4-UR & 46.7 (74.5) & 32.7 (69.7) & 53.5 (79.9) & 45.4 (77.5) & 43.1 (70.9) & 32.9 (68.4) & 42.0 (73.5)\\
M4-Prompts & 54.1 (77.1) & 36.9 (72.1) & 56.9 (81.1) & 47.3 (78.4) & 56.1 (77.5) & 41.0 (73.7) & 48.2 (76.6)\\
M4-Prompts FT & 45.5 (70.1) & 32.5 (67.4) & 48.6 (73.8) & 40.7 (72.8) & 48.1 (70.5) & 36.9 (69.0) & 41.7 (70.6)\\
PaLM 8B & 38.6 (69.8) & 26.7 (65.8) & 45.9 (75.9) & 38.0 (73.6) & 39.3 (69.4) & 32.1 (67.8) & 36.5 (70.4)\\
PaLM 62B & 49.5 (75.9) & 36.7 (72.4) & 55.4 (80.1) & 46.1 (77.8) & 50.3 (75.2) & 41.5 (73.5) & 46.3 (75.8)\\
PaLM 540B & 53.7 (77.1) & \textbf{40.1} (\textbf{73.9}) & \textbf{59.0} (\textbf{81.2}) & \textbf{49.5} (\textbf{79.0}) & 54.8 (76.9) & \textbf{45.6} (\textbf{75.5}) & \textbf{50.2} (77.3)\\
Online & \textbf{56.2} (\textbf{78.7}) & 35.6 (72.3) & 56.3 (\textbf{81.2}) & 46.9 (78.3) & \textbf{65.2} (\textbf{80.5}) & 42.9 (75.0) & 49.8 (\textbf{77.6}) \\
\midrule
& zh-CN & zh-TW & zh-CN & zh-TW & zh-CN & zh-TW & zh\\\midrule
UR & 22.6 (58.5) & 13.8 (56.0) & 26.7 (67.1) & 19.5 (65.3) & 26.4 (62.1) & 20.4 (61.0) & 21.3 (61.7)\\
M4-UR & 33.3 (65.0) & 18.9 (58.2) & 43.2 (73.0) & 31.4 (70.4) & 40.8 (65.4) & 30.8 (63.6) & 32.5 (65.9)\\
M4-Prompts & 33.3 (64.9) & 18.3 (57.6) & 44.2 (72.5) & 32.0 (68.7) & 43.7 (67.0) & 32.2 (63.4) & 33.3 (65.6)\\
M4-Prompts FT & 33.8 (65.7) & 18.8 (59.0) & 44.8 (73.2) & 31.6 (69.8) & 42.7 (66.7) & 31.5 (64.0) & 33.2 (66.4)\\
PaLM 8B & 17.6 (55.7) & 13.3 (52.3) & 28.1 (65.7) & 24.4 (63.9) & 21.6 (56.3) & 18.2 (56.1) & 20.4 (58.3)\\
PaLM 62B & 29.2 (62.2) & 20.4 (59.8) & 40.2 (71.8) & 33.0 (69.9) & 34.5 (64.0) & 26.0 (63.1) & 30.3 (65.1)\\
PaLM 540B & 34.8 (66.5) & \textbf{24.6} (\textbf{63.3}) & 44.9 (74.7) & 35.2 (\textbf{72.5}) & 40.0 (67.8) & 29.6 (66.0) & 34.5 (68.4)\\
Online & \textbf{39.7} (\textbf{68.0}) & 21.9 (61.8) & \textbf{50.4} (\textbf{75.0}) & \textbf{37.0} (72.2) & \textbf{56.1} (\textbf{72.0}) & \textbf{39.9} (\textbf{68.7}) & \textbf{40.1} (\textbf{69.6}) \\
 \bottomrule
\end{tabular}
\caption{FRMT per-bucket test set results, in the format: BLEU (BLEURT). The ``FRMT'' score is the geometric mean across regions of the arithmetic mean across buckets.}
\label{tab:results_auto}
\end{table*}
\section{FRMT Dataset}\label{sec:new-dataset}
We introduce the FRMT dataset for evaluating the quality of few-shot region-aware machine translation. The dataset covers two regions each for Portuguese (Brazil and Portugal) and Mandarin (Mainland and Taiwan).
Our overall approach for constructing the dataset is to sample English sentences from Wikipedia and acquire professional human translations into the target regional varieties.
Final quality control is done through manual evaluation by an independent set of translators, using the MQM protocol \cite{Freitag2021ExpertsEA} that we also employ to evaluate system translation quality.

\subsection{Data sampling method}\label{sec:data_sampling}
FRMT seeks to capture region-specific linguistic differences, as well as potential distractors in the form of entities that are strongly associated with one region, without necessarily having a different linguistic rendering (e.g., Lisbon vs.~S\~{a}o Paulo). To this end, we divide the dataset into three buckets (\texttt{lexical}, \texttt{entity}, \texttt{random}), each containing human translations of sentences extracted from different sets of English Wikipedia articles.\footnote{
As Wikipedia data source we use the training split of {\tt wiki40b} (v1.3.0) by \citet{wiki40b}, available at \scriptsize{\url{https://www.tensorflow.org/datasets/catalog/wiki40b}}.}

\subsection{Human evaluation}\label{sec:human_eval_mqm}
To obtain the highest fidelity human ratings, we use the expert-based 
Multidimensional Quality Metrics (MQM) evaluation framework proposed by \citet{Freitag2021ExpertsEA}. This is the same framework recommended by the WMT'21 Evaluation Campaign \citep{freitag-etal-2021-results}. For our evaluation, expert raters are shown a chunk of 10 contiguous English sentences from our test set with the corresponding translations from one combination of model (or human) and target region. Raters then identify errors in the translations, assigning a category and severity to each; see \citet{Freitag2021ExpertsEA} for details. Due to cost constraints, we evaluate 25\%{} of the test set, evenly distributed across our three evaluation buckets. Each chunk is rated by three raters.

In Mandarin, there is an added complexity of script choice.  Mainland Mandarin is typically written in the ``simplified'' Han script, while Taiwan Mandarin is written in ``traditional'' script.  To disentangle lexical choice from script choice, we define lexical accuracy in a script-agnostic manner.  For example, for the word \textit{pineapple}, if the target region is Taiwan, we count both the (expected) traditional and (unexpected) simplified forms of the Taiwan variant \textit{fènglí} (\trad{鳳梨} and \simp{凤梨}) as correct, while counting both script forms of the Mainland variant \textit{bōluó} (\simp{菠萝} and \trad{菠蘿}) as incorrect.  This ensures that even if a model is outputting the wrong script, it will be rewarded or penalized based on its lexical choices. This also prevents the possibility of ``gaming'' our metric by only using the lexical forms and script of a single region.

\subsection{Reporting FRMT results}\label{sec:recommendations}
For the FRMT \emph{task} (as opposed to the \emph{dataset}), we stipulate a key ``few-shot'' restriction: candidate models \textbf{may not be intentionally exposed to any region-labeled data} at any point during training.  This restriction covers both region-labeled monolingual data as well as region-labeled parallel translation data.\footnote{Models may train on multilingual web crawl data, as is common practice, as long as supervised region labels are not provided.  We allow that some implicit or explicit region labels may appear within the unsupervised data.} While it may not be difficult to obtain region labels for Brazil/Portugal or Mainland/Taiwan (e.g.~by filtering web pages on top-level web domain), we intend for FRMT to serve as a measure of few-shot generalization to \emph{arbitrary} regions and language varieties, for which obtaining labels may be much harder.

Researchers sharing FRMT results should \textbf{report per-bucket BLEU and lexical accuracy} metrics on the test set, as shown in Tables \ref{tab:results_auto} and \ref{tab:lexical_accuracy}. These metrics are efficient to calculate with our provided evaluation scripts.

For Mandarin (on the full 25\% sample), a large gap remains between expert translations and our baselines (Gold: $2.5$ vs.~PaLM: $8.8$).  Our results indicate that better region handling will be needed to close this gap; our baselines are not effectively leveraging region information (as evidenced by small match vs.~mismatch deltas), and even the best-case expert translation scores poorly in the mismatched region (Gold match: $2.5$ vs.~Gold mismatch: $5.2$).\footnote{Note, this is ignoring orthographic differences, as we transliterate outputs to the rater's script.  See section \S\ref{sec:script_analysis} for analysis of script differences.}

Table \ref{tab:results_auto} shows the performance of our baseline models on the automated metrics BLEU and BLEURT\@. The \textbf{``FRMT'' metric} is a summary of per-language performance, calculated as the geometric mean across regions of the arithmetic mean across buckets. Recall, while BLEU scores serve as a lightweight point of comparison, we recommend additionally reporting the more costly BLEURT where possible, as this correlates more strongly with human judgments.
{"value": "37.4", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "32.7", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "46.7", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT Entities pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "40.8", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT Entities pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "39.8", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT Random pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "35.3", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT Random pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "38.7", "type": "Result", "training data/set": "mC4 OPUS", "test data/set": "FRMT pt", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "UR", "model settings": {"xx": "yy"}} {"value": "46.7", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "32.7", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "53.5", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Entities pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "45.4", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Entities pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "43.1", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Random pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "32.9", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Random pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "42.0", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT pt", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-UR", "model settings": {"xx": "yy"}} {"value": "54.1", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "36.9", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "56.9", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Entities pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "47.3", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Entities pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "56.1", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Random pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "41.0", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Random pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "48.2", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT pt", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts", "model settings": {"xx": "yy"}} {"value": "45.5", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "32.5", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "48.6", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Entities pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "40.7", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Entities pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "48.1", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Random pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "36.9", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT Random pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "41.7", "type": "Result", "training data/set": "a mixture of monolingual and parallel data from 112 languages mined from the web", "test data/set": "FRMT pt", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "M4-Prompts FT", "model settings": {"xx": "yy"}} {"value": "38.6", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "26.7", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "45.9", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Entities pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "38.0", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Entities pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "39.3", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Random pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "32.1", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Random pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "36.5", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT pt", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 8B", "model settings": {"xx": "yy"}} {"value": "49.5", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "36.7", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "55.4", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Entities pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "46.1", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Entities pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "50.3", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Random pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "41.5", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Random pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "46.3", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT pt", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 62B", "model settings": {"xx": "yy"}} {"value": "53.7", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Lexical pt-BR", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": {"xx": "yy"}, "model": "PaLM 540B", "model settings": {"xx": "yy"}} {"value": "40.1", "type": "Result", "training data/set": "a multilingual corpus", "test data/set": "FRMT Lexical pt-PT", "task": ["Few-Shot Region-Aware Machine Translation", "FRMT", "Machine Translation"], "metric": "BLEU", "experimental settings": 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