prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Which eight NER tasks did they evaluate on?
Context 1: Introduction
Pretrained Language Models (PTLMs) such as BERT BIBREF1 have spearheaded advances on many NLP tasks  Usually, PTLMs are pretrained on unlabeled general-domain and/or mixed-domain text, such as Wikipedia, digital books or the Common Crawl corpus 
When applying PTLMs to specific domains, it can be useful to domain-adapt them  Domain adaptation of PTLMs has typically been achieved by pretraining on target-domain text  One such model is BioBERT BIBREF2, which was initialized from general-domain BERT and then pretrained on biomedical scientific publications  The domain adaptation is shown to be helpful for target-domain tasks such as biomedical Named Entity Recognition (NER) or Question Answering (QA)  On the downside, the computational cost of pretraining can be considerable: BioBERTv1 0 was adapted for ten days on eight large GPUs (see Table TABREF1), which is expensive, environmentally unfriendly, prohibitive for small research labs and students, and may delay prototyping on emerging domains 
We therefore propose a fast, CPU-only domain-adaptation method for PTLMs: We train Word2Vec BIBREF3 on target-domain text and align the resulting word vectors with the wordpiece vectors of an existing general-domain PTLM  The PTLM thus gains domain-specific lexical knowledge in the form of additional word vectors, but its deeper layers remain unchanged  Since Word2Vec and the vector space alignment are efficient models, the process requires a fraction of the resources associated with pretraining the PTLM itself, and it can be done on CPU 
In Section SECREF4, we use the proposed method to domain-adapt BERT on PubMed+PMC (the data used for BioBERTv1 0) and/or CORD-19 (Covid-19 Open Research Dataset)  We improve over general-domain BERT on eight out of eight biomedical NER tasks, using a fraction of the compute cost associated with BioBERT  In Section SECREF5, we show how to quickly adapt an existing Question Answering model to text about the Covid-19 pandemic, without any target-domain Language Model pretraining or finetuning 
Related work ::: The BERT PTLM
For our purpose, a PTLM consists of three parts: A tokenizer $\mathcal {T}_\mathrm {LM} : \mathbb {L}^+ \rightarrow \mathbb {L}_\mathrm {LM}^+$, a wordpiece embedding function $\mathcal {E}_\mathrm {LM}: \mathbb {L}_\mathrm {LM} \rightarrow \mathbb {R}^{d_\mathrm {LM}}$ and an encoder function $\mathcal {F}_\mathrm {LM}$  $\mathbb {L}_\mathrm {LM}$ is a limited vocabulary of wordpieces  All words that are not in $\mathbb {L}_\mathrm {LM}$ are tokenized into sequences of shorter wordpieces, e g , tachycardia becomes ta ##chy ##card ##ia  Given a sentence $S = [w_1, \ldots , w_T]$, tokenized as $\mathcal {T}_\mathrm {LM}(S) = [\mathcal {T}_\mathrm {LM}(w_1); \ldots ; \mathcal {T}_\mathrm {LM}(w_T)]$, $\mathcal {E}_\mathrm {LM}$ embeds every wordpiece in $\mathcal {T}_\mathrm {LM}(S)$ into a real-valued, trainable wordpiece vector  The wordpiece vectors of the entire sequence are stacked and fed into $\mathcal {F}_\mathrm {LM}$  Note that we consider position and segment embeddings to be a part of $\mathcal {F}_\mathrm {LM}$ rather than $\mathcal {E}_\mathrm {LM}$ 
In the case of BERT, $\mathcal {F}_\mathrm {LM}$ is a Transformer BIBREF4, followed by a final Feed-Forward Net  During pretraining, the Feed-Forward Net predicts the identity of masked wordpieces  When finetuning on a supervised task, it is usually replaced with a randomly initialized task-specific layer 
Related work ::: Domain-adapted PTLMs
Domain adaptation of PTLMs is typically achieved by pretraining on unlabeled target-domain text  Some examples of such models are BioBERT BIBREF2, which was pretrained on the PubMed and/or PubMed Central (PMC) corpora, SciBERT BIBREF5, which was pretrained on papers from SemanticScholar, ClinicalBERT BIBREF6, BIBREF7 and ClinicalXLNet BIBREF8, which were pretrained on clinical patient notes, and AdaptaBERT BIBREF9, which was pretrained on Early Modern English text  In most cases, a domain-adapted PTLM is initialized from a general-domain PTLM (e g , standard BERT), though BIBREF5 report better results with a model that was pretrained from scratch with a custom wordpiece vocabulary  In this paper, we focus on BioBERT, as its domain adaptation corpora are publicly available 
Related work ::: Word vectors
Word vectors are distributed representations of words that are trained on unlabeled text  Contrary to PTLMs, word vectors are non-contextual, i e , a word type is always assigned the same vector, regardless of context  In this paper, we use Word2Vec BIBREF3 to train word vectors  We will denote the Word2Vec lookup function as $\mathcal {E}_\mathrm {W2V} : \mathbb {L}_\mathrm {W2V} \rightarrow \mathbb {R}^{d_\mathrm {W2V}}$ 
Related work ::: Word vector space alignment
Word vector space alignment has most frequently been explored in the context of cross-lingual word embeddings  For instance, BIBREF10 align English and Spanish Word2Vec spaces by a simple linear transformation  BIBREF11 use a related method to align cross-lingual word vectors and multilingual BERT wordpiece vectors 
Method
In the following, we assume access to a general-domain PTLM, as described in Section SECREF2, and a corpus of unlabeled target-domain text 
Method ::: Creating new input vectors
In a first step, we train Word2Vec on the target-domain corpus  In a second step, we take the intersection of $\mathbb {L}_\mathrm {LM}$ and $\mathbb {L}_\mathrm {W2V}$  In practice, the intersection mostly contains wordpieces from $\mathbb {L}_\mathrm {LM}$ that correspond to standalone words  It also contains single characters and other noise, however, we found that filtering them does not improve alignment quality  In a third step, we use the intersection to fit an unconstrained linear transformation $\mathbf {W} \in \mathbb {R}^{d_\mathrm {LM} \times d_\mathrm {W2V}}$ via least squares:
Intuitively, $\mathbf {W}$ makes Word2Vec vectors “look like” the PTLM's native wordpiece vectors, just like cross-lingual alignment makes word vectors from one language “look like” word vectors from another language  In Table TABREF7 (top), we show examples of within-space and cross-space nearest neighbors after alignment 
Method ::: Updating the wordpiece embedding layer
Next, we redefine the wordpiece embedding layer of the PTLM  The most radical strategy would be to replace the entire layer with the aligned Word2Vec vectors:
In initial experiments, this strategy led to a drop in performance, presumably because function words are not well represented by Word2Vec, and replacing them disrupts BERT's syntactic abilities  To prevent this problem, we leave existing wordpiece vectors intact and only add new ones:
Method ::: Updating the tokenizer
In a final step, we update the tokenizer to account for the added words  Let $\mathcal {T}_\mathrm {LM}$ be the standard BERT tokenizer, and let $\hat{\mathcal {T}}_\mathrm {LM}$ be the tokenizer that treats all words in $\mathbb {L}_\mathrm {LM} \cup \mathbb {L}_\mathrm {W2V}$ as one-wordpiece tokens, while tokenizing any other words as usual 
In practice, a given word may or may not benefit from being tokenized by $\hat{\mathcal {T}}_\mathrm {LM}$ instead of $\mathcal {T}_\mathrm {LM}$  To give a concrete example, 82% of the words in the BC5CDR NER dataset that end in the suffix -ia are inside a disease entity (e g , tachycardia)  $\mathcal {T}_\mathrm {LM}$ tokenizes this word as ta ##chy ##card ##ia, thereby exposing the orthographic cue to the model  As a result, $\mathcal {T}_\mathrm {LM}$ leads to higher recall on -ia diseases  But there are many cases where wordpiece tokenization is meaningless or misleading  For instance euthymia (not a disease) is tokenized by $\mathcal {T}_\mathrm {LM}$ as e ##uth ##ym ##ia, making it likely to be classified as a disease  By contrast, $\hat{\mathcal {T}}_\mathrm {LM}$ gives euthymia a one-wordpiece representation that depends only on distributional semantics  We find that using $\hat{\mathcal {T}}_\mathrm {LM}$ improves precision on -ia diseases 
To combine these complementary strengths, we use a 50/50 mixture of $\mathcal {T}_\mathrm {LM}$-tokenization and $\hat{\mathcal {T}}_\mathrm {LM}$-tokenization when finetuning the PTLM on a task  At test time, we use both tokenizers and mean-pool the outputs  Let $o(\mathcal {T}(S))$ be some output of interest (e g , a logit), given sentence $S$ tokenized by $\mathcal {T}$  We predict:
Experiment 1: Biomedical NER
In this section, we use the proposed method to create GreenBioBERT, an inexpensive and environmentally friendly alternative to BioBERT  Recall that BioBERTv1 0 (biobert_v1 0_pubmed_pmc) was initialized from general-domain BERT (bert-base-cased) and pretrained on PubMed+PMC 
Experiment 1: Biomedical NER ::: Domain adaptation
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 768$ on PubMed+PMC (see Appendix for details)  Then, we follow the procedure described in Section SECREF3 to update the wordpiece embedding layer and tokenizer of general-domain BERT 
Experiment 1: Biomedical NER ::: Finetuning
We finetune GreenBioBERT on the eight publicly available NER tasks used in BIBREF2  We also do reproduction experiments with general-domain BERT and BioBERTv1 0, using the same setup as our model  We average results over eight random seeds  See Appendix for details on preprocessing, training and hyperparameters 
Experiment 1: Biomedical NER ::: Results and discussion
Table TABREF7 (bottom) shows entity-level precision, recall and F1  For ease of visualization, Figure FIGREF13 shows what portion of the BioBERT – BERT F1 delta is covered  We improve over general-domain BERT on all tasks with varying effect sizes  Depending on the points of reference, we cover an average 52% to 60% of the BioBERT – BERT F1 delta (54% for BioBERTv1 0, 60% for BioBERTv1 1 and 52% for our reproduction experiments)  Table TABREF17 (top) shows the importance of vector space alignment: If we replace the aligned Word2Vec vectors with their non-aligned counterparts (by setting $\mathbf {W} = \mathbf {1}$) or with randomly initialized vectors, F1 drops on all tasks 
Experiment 2: Covid-19 QA
In this section, we use the proposed method to quickly adapt an existing general-domain QA model to an emerging target domain: Covid-19  Our baseline model is SQuADBERT (bert-large-uncased-whole-word-masking-finetuned-squad), a version of BERT that was finetuned on general-domain SQuAD BIBREF19  We evaluate on Deepset-AI Covid-QA, a SQuAD-style dataset with 1380 questions (see Appendix for details on data and preprocessing)  We assume that there is no target-domain finetuning data, which is a realistic setup for a new domain 
Experiment 2: Covid-19 QA ::: Domain adaptation
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 1024$ on CORD-19 (Covid-19 Open Research Dataset) and/or PubMed+PMC  The process takes less than an hour on CORD-19 and about one day on the combined corpus, again without the need for a GPU  Then, we update SQuADBERT's wordpiece embedding layer and tokenizer, as described in Section SECREF3  We refer to the resulting model as GreenCovidSQuADBERT 
Experiment 2: Covid-19 QA ::: Results and discussion
Table TABREF17 (bottom) shows that GreenCovidSQuADBERT outperforms general-domain SQuADBERT in all metrics  Most of the improvement can be achieved with just the small CORD-19 corpus, which is more specific to the target domain (compare “Cord-19 only” and “Cord-19+PubMed+PMC”) 
Conclusion
As a reaction to the trend towards high-resource models, we have proposed an inexpensive, CPU-only method for domain-adapting Pretrained Language Models: We train Word2Vec vectors on target-domain data and align them with the wordpiece vector space of a general-domain PTLM 
On eight biomedical NER tasks, we cover over 50% of the BioBERT – BERT F1 delta, at 5% of BioBERT's domain adaptation CO$_2$ footprint and 2% of its cloud compute cost  We have also shown how to rapidly adapt an existing BERT QA model to an emerging domain – the Covid-19 pandemic – without the need for target-domain Language Model pretraining or finetuning 
We hope that our approach will benefit practitioners with limited time or resources, and that it will encourage environmentally friendlier NLP 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Word2Vec training
We downloaded the PubMed, PMC and CORD-19 corpora from:
https://ftp ncbi nlm nih gov/pub/pmc/oa_bulk/ [20 January 2020, 68GB raw text]
https://ftp ncbi nlm nih gov/pubmed/baseline/ [20 January 2020, 24GB raw text]
https://pages semanticscholar org/coronavirus-research [17 April 2020, 2GB raw text]
We extract all abstracts and text bodies and apply the BERT basic tokenizer (a word tokenizer that standard BERT uses before wordpiece tokenization)  Then, we train CBOW Word2Vec with negative sampling  We use default parameters except for the vector size (which we set to $d_\mathrm {W2V} = d_\mathrm {LM}$) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Pretrained models
General-domain BERT and BioBERTv1 0 were downloaded from:
https://storage googleapis com/bert_models/2018_10_18/cased_L-12_H-768_A-12 zip
https://github com/naver/biobert-pretrained
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Data
We downloaded the NER datasets by following instructions on https://github com/dmis-lab/biobert#Datasets  For detailed dataset statistics, see BIBREF2 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Preprocessing
We cut all sentences into chunks of 30 or fewer whitespace-tokenized words (without splitting inside labeled spans)  Then, we tokenize every chunk $S$ with $\mathcal {T} = \mathcal {T}_\mathrm {LM}$ or $\mathcal {T} = \hat{\mathcal {T}}_\mathrm {LM}$ and add special tokens:
Word-initial wordpieces in $\mathcal {T}(S)$ are labeled as B(egin), I(nside) or O(utside), while non-word-initial wordpieces are labeled as X(ignore) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Modeling, training and inference
We follow BIBREF2's implementation (https://github com/dmis-lab/biobert): We add a randomly initialized softmax classifier on top of the last BERT layer to predict the labels  We finetune the entire model to minimize negative log likelihood, with the standard Adam optimizer BIBREF20 and a linear learning rate scheduler (10% warmup)  Like BIBREF2, we finetune on the concatenation of the training and development set  All finetuning runs were done on a GeForce Titan X GPU (12GB) 
Since we do not have the resources for an extensive hyperparameter search, we use defaults and recommendations from the BioBERT repository: Batch size of 32, peak learning rate of $1 \cdot 10^{-5}$, and 100 epochs 
At inference time, we gather the output logits of word-initial wordpieces only  Since the number of word-initial wordpieces is the same for $\mathcal {T}_\mathrm {LM}(S)$ and $\hat{\mathcal {T}}_\mathrm {LM}(S)$, this makes mean-pooling the logits straightforward 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Note on our reproduction experiments
We found it easier to reproduce or exceed BIBREF2's results for general-domain BERT, compared to their results for BioBERTv1 0 (see Figure FIGREF13, main paper)  While this may be due to hyperparameters, it suggests that BioBERTv1 0 was more strongly tuned than BERT in the original BioBERT paper  This observation does not affect our conclusions, as GreenBioBERT performs better than reproduced BERT as well 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Pretrained model
We downloaded the SQuADBERT baseline from:
https://huggingface co/bert-large-uncased-whole-word-masking-finetuned-squad
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Data
We downloaded the Deepset-AI Covid-QA dataset from:
https://github com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA json [24 April 2020]
At the time of writing, the dataset contains 1380 questions and gold answer spans  Every question is associated with one of 98 research papers (contexts)  We treat the entire dataset as a test set 
Note that there are some important differences between the dataset and SQuAD, which make the task challenging:
The contexts are full documents rather than single paragraphs  Thus, the correct answer may appear several times, often with slightly different wordings  Only a single one of the occurrences is annotated as correct, e g :
What was the prevalence of Coronavirus OC43 in community samples in Ilorin, Nigeria?
13 3% (95% CI 6 9-23 6%) # from main text
(13 3%, 10/75)  # from abstract
SQuAD gold answers are defined as the “shortest span in the paragraph that answered the question” BIBREF19, but many Covid-QA gold answers are longer and contain non-essential context, e g :
When was the Middle East Respiratory Syndrome Coronavirus isolated first?
(MERS-CoV) was first isolated in 2012, in a 60-year-old man who died in Jeddah, KSA due to severe acute pneumonia and multiple organ failure
2012,
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Preprocessing
We tokenize every question-context pair $(Q, C)$ with $\mathcal {T} = \mathcal {T}_\mathrm {LM}$ or $\mathcal {T} = \hat{\mathcal {T}}_\mathrm {LM}$, which yields $(\mathcal {T}(Q), \mathcal {T}(C))$  Since $\mathcal {T}(C)$ is usually too long to be digested in a single forward pass, we define a sliding window with width and stride $N = \mathrm {floor}(\frac{509 - |\mathcal {T}(Q)|}{2})$  At step $n$, the “active” window is between $a^{(l)}_n = nN$ and $a^{(r)}_n = \mathrm {min}(|C|, nN+N)$  The input is defined as:
$p^{(l)}_n$ and $p^{(r)}_n$ are chosen such that $|X^{(n)}| = 512$, and such that the active window is in the center of the input (if possible) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Modeling and inference
Feeding $X^{(n)}$ into the pretrained QA model yields start logits $\mathbf {h^{\prime }}^{(\mathrm {start}, n)} \in \mathbb {R}^{|X^{(n)}|}$ and end logits $\mathbf {h^{\prime }}^{(\mathrm {end},n)} \in \mathbb {R}^{|X^{(n)}|}$  We extract and concatenate the slices that correspond to the active windows of all steps:
Next, we map the logits from the wordpiece level to the word level  This allows us to mean-pool the outputs of $\mathcal {T}_\mathrm {LM}$ and $\hat{\mathcal {T}}_\mathrm {LM}$ even when $|\mathcal {T}_\mathrm {LM}(C)| \ne |\hat{\mathcal {T}}_\mathrm {LM}(C)|$ 
Let $c_i$ be a whitespace-delimited word in $C$  Let $\mathcal {T}(C)_{j:j+|\mathcal {T}(c_i)|}$ be the corresponding wordpieces  The start and end logits of $c_i$ are derived as:
Finally, we return the answer span $C_{k:k^{\prime }}$ that maximizes $o^{(\mathrm {start})}_k + o^{(\mathrm {end})}_{k^{\prime }}$, subject to the constraints that $k^{\prime }$ does not precede $k$ and the answer span is not longer than 500 characters 
Context 2: Introduction
Bidirectional Encoder Representations from Transformers (BERT) is a novel Transformer BIBREF0 model, which recently achieved state-of-the-art performance in several language understanding tasks, such as question answering, natural language inference, semantic similarity, sentiment analysis, and others BIBREF1  While well-suited to dealing with relatively short sequences, Transformers suffer from a major issue that hinders their applicability in classification of long sequences, i e  they are able to consume only a limited context of symbols as their input BIBREF2 
There are several natural language (NLP) processing tasks that involve such long sequences  Of particular interest are topic identification of spoken conversations BIBREF3, BIBREF4, BIBREF5 and call center customer satisfaction prediction BIBREF6, BIBREF7, BIBREF8, BIBREF9  Call center conversations, while usually quite short and to the point, often involve agents trying to solve very complex issues that the customers experience, resulting in some calls taking even an hour or more  For speech analytics purposes, these calls are typically transcribed using an automatic speech recognition (ASR) system, and processed in textual representations further down the NLP pipeline  These transcripts sometimes exceed the length of 5000 words  Furthermore, temporal information might play an important role in tasks like CSAT  For example, a customer may be angry at the beginning of the call, but after her issue is resolved, she would be very satisfied with the way it was handled  Therefore, simple bag of words models, or any model that does not include temporal dependencies between the inputs, may not be well-suited to handle this category of tasks  This motivates us to employ model such as BERT in this task 
In this paper, we propose a method that builds upon BERT's architecture  We split the input text sequence into shorter segments in order to obtain a representation for each of them using BERT  Then, we use either a recurrent LSTM BIBREF10 network, or another Transformer, to perform the actual classification  We call these techniques Recurrence over BERT (RoBERT) and Transformer over BERT (ToBERT)  Given that these models introduce a hierarchy of representations (segment-wise and document-wise), we refer to them as Hierarchical Transformers  To the best of our knowledge, no attempt has been done before to use the Transformer architecture for classification of such long sequences 
Our novel contributions are:
Two extensions - RoBERT and ToBERT - to the BERT model, which enable its application in classification of long texts by performing segmentation and using another layer on top of the segment representations 
State-of-the-art results on the Fisher topic classification task 
Significant improvement on the CSAT prediction task over the MS-CNN model 
Related work
Several dimensionality reduction algorithms such as RBM, autoencoders, subspace multinomial models (SMM) are used to obtain a low dimensional representation of documents from a simple BOW representation and then classify it using a simple linear classifiers BIBREF11, BIBREF12, BIBREF13, BIBREF4  In BIBREF14 hierarchical attention networks are used for document classification  They evaluate their model on several datasets with average number of words around 150  Character-level CNN are explored in BIBREF15 but it is prohibitive for very long documents  In BIBREF16, dataset collected from arXiv papers is used for classification  For classification, they sample random blocks of words and use them together for classification instead of using full document which may work well as arXiv papers are usually coherent and well written on a well defined topic  Their method may not work well on spoken conversations as random block of words usually do not represent topic of full conversation 
Several researchers addressed the problem of predicting customer satisfaction BIBREF6, BIBREF7, BIBREF8, BIBREF9  In most of these works, logistic regression, SVM, CNN are applied on different kinds of representations 
In BIBREF17, authors use BERT for document classification but the average document length is less than BERT maximum length 512  TransformerXL BIBREF2 is an extension to the Transformer architecture that allows it to better deal with long inputs for the language modelling task  It relies on the auto-regressive property of the model, which is not the case in our tasks 
Method ::: BERT
Because our work builds heavily upon BERT, we provide a brief summary of its features  BERT is built upon the Transformer architecture BIBREF0, which uses self-attention, feed-forward layers, residual connections and layer normalization as the main building blocks  It has two pre-training objectives:
Masked language modelling - some of the words in a sentence are being masked and the model has to predict them based on the context (note the difference from the typical autoregressive language model training objective);
Next sentence prediction - given two input sequences, decide whether the second one is the next sentence or not 
BERT has been shown to beat the state-of-the-art performance on 11 tasks with no modifications to the model architecture, besides adding a task-specific output layer BIBREF1  We follow same procedure suggested in BIBREF1 for our tasks  Fig  FIGREF8 shows the BERT model for classification  We obtain two kinds of representation from BERT: pooled output from last transformer block, denoted by H, and posterior probabilities, denoted by P  There are two variants of BERT - BERT-Base and BERT-Large  In this work we are using BERT-Base for faster training and experimentation, however, our methods are applicable to BERT-Large as well  BERT-Base and BERT-Large are different in model parameters such as number of transformer blocks, number of self-attention heads  Total number of parameters in BERT-Base are 110M and 340M in BERT-Large 
BERT suffers from major limitations in terms of handling long sequences  Firstly, the self-attention layer has a quadratic complexity $O(n^2)$ in terms of the sequence length $n$ BIBREF0  Secondly, BERT uses a learned positional embeddings scheme BIBREF1, which means that it won't likely be able to generalize to positions beyond those seen in the training data 
To investigate the effect of fine-tuning BERT on task performance, we use either the pre-trained BERT weights, or the weights from a BERT fine-tuned on the task-specific dataset on a segment-level (i e  we preserve the original label but fine-tune on each segment separately instead of on the whole text sequence)  We compare these results to using the fine-tuned segment-level BERT predictions directly as inputs to the next layer 
Method ::: Recurrence over BERT
Given that BERT is limited to a particular input length, we split the input sequence into segments of a fixed size with overlap  For each of these segments, we obtain H or P from BERT model  We then stack these segment-level representations into a sequence, which serves as input to a small (100-dimensional) LSTM layer  Its output serves as a document embedding  Finally, we use two fully connected layers with ReLU (30-dimensional) and softmax (the same dimensionality as the number of classes) activations to obtain the final predictions 
With this approach, we overcome BERT's computational complexity, reducing it to $O(n/k * k^2) = O(nk)$ for RoBERT, with $k$ denoting the segment size (the LSTM component has negligible linear complexity $O(k)$)  The positional embeddings are also no longer an issue 
Method ::: Transformer over BERT
Given that Transformers' edge over recurrent networks is their ability to effectively capture long distance relationships between words in a sequence BIBREF0, we experiment with replacing the LSTM recurrent layer in favor of a small Transformer model (2 layers of transformer building block containing self-attention, fully connected, etc )  To investigate if preserving the information about the input sequence order is important, we also build a variant of ToBERT which learns positional embeddings at the segment-level representations (but is limited to sequences of length seen during the training) 
ToBERT's computational complexity $O(\frac{n^2}{k^2})$ is asymptotically inferior to RoBERT, as the top-level Transformer model again suffers from quadratic complexity in the number of segments  However, in practice this number is much smaller than the input sequence length (${\frac{n}{k}} << n$), so we haven't observed performance or memory issues with our datasets 
Experiments
We evaluated our models on 3 different datasets:
CSAT dataset for CSAT prediction, consisting of spoken transcripts (automatic via ASR) 
20 newsgroups for topic identification task, consisting of written text;
Fisher Phase 1 corpus for topic identification task, consisting of spoken transcripts (manual);
Experiments ::: CSAT
CSAT dataset consists of US English telephone speech from call centers  For each call in this dataset, customers participated in that call gave a rating on his experience with agent  Originally, this dataset has labels rated on a scale 1-9 with 9 being extremely satisfied and 1 being extremely dissatisfied  Fig  FIGREF16 shows the histogram of ratings for our dataset  As the distribution is skewed towards extremes, we choose to do binary classification with ratings above 4 5 as satisfied and below 4 5 as dissatisfied  Quantization of ratings also helped us to create a balanced dataset  This dataset contains 4331 calls and we split them into 3 sets for our experiments: 2866 calls for training, 362 calls for validation and, finally, 1103 calls for testing 
We obtained the transcripts by employing an ASR system  The ASR system uses TDNN-LSTM acoustic model trained on Fisher and Switchboard datasets with lattice-free maximum mutual information criterion BIBREF18
Context 3: Introduction
Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 has enabled end-to-end training of a translation system without needing to deal with word alignments, translation rules, and complicated decoding algorithms, which are the characteristics of phrase-based statistical machine translation (PBSMT) BIBREF3   Although NMT can be significantly better than PBSMT in resource-rich scenarios, PBSMT performs better in low-resource scenarios BIBREF4   Only by exploiting cross-lingual transfer learning techniques BIBREF5 , BIBREF6 , BIBREF7 , can the NMT performance approach PBSMT performance in low-resource scenarios 
However, such methods usually require an NMT model trained on a resource-rich language pair like French INLINEFORM0 English (parent), which is to be fine-tuned for a low-resource language pair like Uzbek INLINEFORM1 English (child)  On the other hand, multilingual approaches BIBREF8 propose to train a single model to translate multiple language pairs  However, these approaches are effective only when the parent target or source language is relatively resource-rich like English (En)  Furthermore, the parents and children models should be trained on similar domains; otherwise, one has to take into account an additional problem of domain adaptation BIBREF9  
In this paper, we work on a linguistically distant and thus challenging language pair Japanese INLINEFORM0 Russian (Ja INLINEFORM1 Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor  Furthermore, the amount of indirect in-domain parallel corpora, i e , Ja INLINEFORM2 En and Ru INLINEFORM3 En, are also small  As we demonstrate in Section SECREF4 , this severely limits the performance of prominent low-resource techniques, such as multilingual modeling, back-translation, and pivot-based PBSMT  To remedy this, we propose a novel multistage fine-tuning method for NMT that combines multilingual modeling BIBREF8 and domain adaptation BIBREF9  
We have addressed two important research questions (RQs) in the context of extremely low-resource machine translation (MT) and our explorations have derived rational contributions (CTs) as follows:
To the best of our knowledge, we are the first to perform such an extensive evaluation of extremely low-resource MT problem and propose a novel multilingual multistage fine-tuning approach involving multilingual modeling and domain adaptation to address it 
Our Japanese–Russian Setting
In this paper, we deal with Ja INLINEFORM0 Ru news translation  This language pair is very challenging because the languages involved have completely different writing system, phonology, morphology, grammar, and syntax  Among various domains, we experimented with translations in the news domain, considering the importance of sharing news between different language speakers  Moreover, news domain is one of the most challenging tasks, due to large presence of out-of-vocabulary (OOV) tokens and long sentences  To establish and evaluate existing methods, we also involved English as the third language  As direct parallel corpora are scarce, involving a language such as English for pivoting is quite common BIBREF10  
There has been no clean held-out parallel data for Ja INLINEFORM0 Ru and Ja INLINEFORM1 En news translation  Therefore, we manually compiled development and test sets using News Commentary data as a source  Since the given Ja INLINEFORM2 Ru and Ja INLINEFORM3 En data share many lines in the Japanese side, we first compiled tri-text data  Then, from each line, corresponding parts across languages were manually identified, and unaligned parts were split off into a new line  Note that we have never merged two or more lines  As a result, we obtained 1,654 lines of data comprising trilingual, bilingual, and monolingual segments (mainly sentences) as summarized in Table TABREF8   Finally, for the sake of comparability, we randomly chose 600 trilingual sentences to create a test set, and concatenated the rest of them and bilingual sentences to form development sets 
Our manually aligned development and test sets are publicly available 
Related Work
koehn-knowles:2017:NMT showed that NMT is unable to handle low-resource language pairs as opposed to PBSMT  Transfer learning approaches BIBREF5 , BIBREF6 , BIBREF7 work well when a large helping parallel corpus is available  This restricts one of the source or the target languages to be English which, in our case, is not possible  Approaches involving bi-directional NMT modeling is shown to drastically improve low-resource translation BIBREF11   However, like most other, this work focuses on translation from and into English 
Remaining options include (a) unsupervised MT BIBREF12 , BIBREF13 , BIBREF14 , (b) parallel sentence mining from non-parallel or comparable corpora BIBREF15 , BIBREF16 , (c) generating pseudo-parallel data BIBREF17 , and (d) MT based on pivot languages BIBREF10   The linguistic distance between Japanese and Russian makes it extremely difficult to learn bilingual knowledge, such as bilingual lexicons and bilingual word embeddings  Unsupervised MT is thus not promising yet, due to its heavy reliance on accurate bilingual word embeddings  Neither does parallel sentence mining, due to the difficulty of obtaining accurate bilingual lexicons  Pseudo-parallel data can be used to augment existing parallel corpora for training, and previous work has reported that such data generated by so-called back-translation can substantially improve the quality of NMT  However, this approach requires base MT systems that can generate somewhat accurate translations  It is thus infeasible in our scenario, because we can obtain only a weak system which is the consequence of an extremely low-resource situation  MT based on pivot languages requires large in-domain parallel corpora involving the pivot languages  This technique is thus infeasible, because the in-domain parallel corpora for Ja INLINEFORM0 En and Ru INLINEFORM1 En pairs are also extremely limited, whereas there are large parallel corpora in other domains  Section SECREF4 empirically confirms the limit of these existing approaches 
Fortunately, there are two useful transfer learning solutions using NMT: (e) multilingual modeling to incorporate multiple language pairs into a single model BIBREF8 and (f) domain adaptation to incorporate out-of-domain data BIBREF9   In this paper, we explore a novel method involving step-wise fine-tuning to combine these two methods  By improving the translation quality in this way, we can also increase the likelihood of pseudo-parallel data being useful to further improve translation quality 
Limit of Using only In-domain Data
This section answers our first research question, [RQ1], about the translation quality that we can achieve using existing methods and in-domain parallel and monolingual data  We then use the strongest model to conduct experiments on generating and utilizing back-translated pseudo-parallel data for augmenting NMT  Our intention is to empirically identify the most effective practices as well as recognize the limitations of relying only on in-domain parallel corpora 
Data
To train MT systems among the three languages, i e , Japanese, Russian, and English, we used all the parallel data provided by Global Voices, more specifically those available at OPUS  Table TABREF9 summarizes the size of train/development/test splits used in our experiments  The number of parallel sentences for Ja INLINEFORM0 Ru is 12k, for Ja INLINEFORM1 En is 47k, and for Ru INLINEFORM2 En is 82k  Note that the three corpora are not mutually exclusive: 9k out of 12k sentences in the Ja INLINEFORM3 Ru corpus were also included in the other two parallel corpora, associated with identical English translations  This puts a limit on the positive impact that the helping corpora can have on the translation quality 
Even when one focuses on low-resource language pairs, we often have access to larger quantities of in-domain monolingual data of each language  Such monolingual data are useful to improve quality of MT, for example, as the source of pseudo-parallel data for augmenting training data for NMT BIBREF17 and as the training data for large and smoothed language models for PBSMT BIBREF4   Table TABREF13 summarizes the statistics on our monolingual corpora for several domains including the news domain  Note that we removed from the Global Voices monolingual corpora those sentences that are already present in the parallel corpus 
https://dumps wikimedia org/backup-index html (20180501) http://www statmt org/wmt18/translation-task html https://www yomiuri co jp/database/glossary/ http://www cs jhu edu/~kevinduh/a/multitarget-tedtalks/ http://opus nlpl eu/Tatoeba-v2 php
We tokenized English and Russian sentences using tokenizer

ground_truth: BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800

BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What datasets did they use for evaluation?
Context 2:  The word error rates using four-gram language models were 9 2% and 17 3% respectively on Switchboard and CallHome portions of Eval2000 dataset 
Experiments ::: 20 newsgroups
20 newsgroups data set is one of the frequently used datasets in the text processing community for text classification and text clustering  This data set contains approximately 20,000 English documents from 20 topics to be identified, with 11314 documents for training and 7532 for testing  In this work, we used only 90% of documents for training and the remaining 10% for validation  For fair comparison with other publications, we used 53160 words vocabulary set available in the datasets website 
Experiments ::: Fisher
Fisher Phase 1 US English corpus is often used for automatic speech recognition in speech community  In this work, we used it for topic identification as in BIBREF3  The documents are 10-minute long telephone conversations between two people discussing a given topic  We used same training and test splits as BIBREF3 in which 1374 and 1372 documents are used for training and testing respectively  For validation of our model, we used 10% of training dataset and the remaining 90% was used for actual model training  The number of topics in this data set is 40 
Experiments ::: Dataset Statistics
Table TABREF22 shows statistics of our datasets  It can be observed that average length of Fisher is much higher than 20 newsgroups and CSAT  Cumulative distribution of document lengths for each dataset is shown in Fig  FIGREF21  It can be observed that almost all of the documents in Fisher dataset have length more than 1000 words  For CSAT, more than 50% of the documents have length greater than 500 and for 20newsgroups only 10% of the documents have length greater than 500  Note that, for CSAT and 20newsgroups, there are few documents with length more than 5000 
Experiments ::: Architecture and Training Details
In this work, we split document into segments of 200 tokens with a shift of 50 tokens to extract features from BERT model  For RoBERT, LSTM model is trained to minimize cross-entropy loss with Adam optimizer BIBREF19  The initial learning rate is set to $0 001$ and is reduced by a factor of $0 95$ if validation loss does not decrease for 3-epochs  For ToBERT, the Transformer is trained with the default BERT version of Adam optimizer BIBREF1 with an initial learning rate of $5e$-5  We report accuracy in all of our experiments  We chose a model with the best validation accuracy to calculate accuracy on the test set  To accomodate for non-determinism of some TensorFlow GPU operations, we report accuracy averaged over 5 runs 
Results
Table TABREF25 presents results using pre-trained BERT features  We extracted features from the pooled output of final transformer block as these were shown to be working well for most of the tasks BIBREF1  The features extracted from a pre-trained BERT model without any fine-tuning lead to a sub-par performance  However, We also notice that ToBERT model exploited the pre-trained BERT features better than RoBERT  It also converged faster than RoBERT  Table TABREF26 shows results using features extracted after fine-tuning BERT model with our datasets  Significant improvements can be observed compared to using pre-trained BERT features  Also, it can be noticed that ToBERT outperforms RoBERT on Fisher and 20newsgroups dataset by 13 63% and 0 81% respectively  On CSAT, ToBERT performs slightly worse than RoBERT but it is not statistically significant as this dataset is small 
Table TABREF27 presents results using fine-tuned BERT predictions instead of the pooled output from final transformer block  For each document, having obtained segment-wise predictions we can obtain final prediction for the whole document in three ways:
Compute the average of all segment-wise predictions and find the most probable class;
Find the most frequently predicted class;
Train a classification model 
It can be observed from Table TABREF27 that a simple averaging operation or taking most frequent predicted class works competitively for CSAT and 20newsgroups but not for the Fisher dataset  We believe the improvements from using RoBERT or ToBERT, compared to simple averaging or most frequent operations, are proportional to the fraction of long documents in the dataset  CSAT and 20newsgroups have (on average) significantly shorter documents than Fisher, as seen in Fig  FIGREF21  Also, significant improvements for Fisher could be because of less confident predictions from BERT model as this dataset has 40 classes  Fig  FIGREF31 presents the comparison of average voting and ToBERT for various document length ranges for Fisher dataset  We used fine-tuned BERT segment-level predictions (P) for this analysis  It can be observed that ToBERT outperforms average voting in every interval  To the best of our knowledge, this is a state-of-the-art result reported on the Fisher dataset 
Table TABREF32 presents the effect of position embeddings on the model performance  It can be observed that position embeddings did not significantly affect the model performance for Fisher and 20newsgroups, but they helped slightly in CSAT prediction (an absolute improvement of 0 64% F1-score)  We think that this is explained by the fact that Fisher and 20newsgroups are topic identification tasks, and the topic does not change much throughout these documents  However, CSAT may vary during the call, and in some cases a naive assumption that the sequential nature of the transcripts is irrelevant may lead to wrong conclusions 
Table TABREF33 compares our results with previous works  It can be seen that our model ToBERT outperforms CNN based experiments by significant margin on CSAT and Fisher datasets  For CSAT dataset, we used multi-scale CNN (MS-CNN) as the baseline, given its strong results on Fisher and 20newsgroups  The setup was replicated from BIBREF5 for comparison  We also see that our result on 20 newsgroups is 0 6% worse than the state-of-the-art 
Conclusions
In this paper, we presented two methods for long documents using BERT model: RoBERT and ToBERT  We evaluated our experiments on two classification tasks - customer satisfaction prediction and topic identification - using 3 datasets: CSAT, 20newsgroups and Fisher  We observed that ToBERT outperforms RoBERT on pre-trained BERT features and fine-tuned BERT features for all our tasks  Also, we noticed that fine-tuned BERT performs better than pre-trained BERT  We have shown that both RoBERT and ToBERT improved the simple baselines of taking an average (or the most frequent) of segment-wise predictions for long documents to obtain final prediction  Position embeddings did not significantly affect our models performance, but slightly improved the accuracy on the CSAT task  We obtained the best results on Fisher dataset and good improvements for CSAT task compared to the CNN baseline  It is interesting to note that the longer the average input in a given task, the bigger improvement we observe w r t  the baseline for that task  Our results confirm that both RoBERT and ToBERT can be used for long sequences with competitive performance and quick fine-tuning procedure  For future work, we shall focus on training models on long documents directly (i e  in an end-to-end manner) 
Context 3: perl of Moses BIBREF3   To tokenize Japanese sentences, we used MeCab with the IPA dictionary  After tokenization, we eliminated duplicated sentence pairs and sentences with more than 100 tokens for all the languages 
MT Methods Examined
We began with evaluating standard MT paradigms, i e , PBSMT BIBREF3 and NMT BIBREF1   As for PBSMT, we also examined two advanced methods: pivot-based translation relying on a helping language BIBREF10 and induction of phrase tables from monolingual data BIBREF14  
As for NMT, we compared two types of encoder-decoder architectures: attentional RNN-based model (RNMT) BIBREF2 and the Transformer model BIBREF18   In addition to standard uni-directional modeling, to cope with the low-resource problem, we examined two multi-directional models: bi-directional model BIBREF11 and multi-to-multi (M2M) model BIBREF8  
After identifying the best model, we also examined the usefulness of a data augmentation method based on back-translation BIBREF17  
First, we built a PBSMT system for each of the six translation directions  We obtained phrase tables from parallel corpus using SyMGIZA++ with the grow-diag-final heuristics for word alignment, and Moses for phrase pair extraction  Then, we trained a bi-directional MSD (monotone, swap, and discontinuous) lexicalized reordering model  We also trained three 5-gram language models, using KenLM on the following monolingual data: (1) the target side of the parallel data, (2) the concatenation of (1) and the monolingual data from Global Voices, and (3) the concatenation of (1) and all monolingual data in the news domain in Table TABREF13  
Subsequently, using English as the pivot language, we examined the following three types of pivot-based PBSMT systems BIBREF10 , BIBREF19 for each of Ja INLINEFORM0 Ru and Ru INLINEFORM1 Ja 
2-step decoding using the source-to-English and English-to-target systems 
Obtain a new phrase table from synthetic parallel data generated by translating English side of the target–English training parallel data to the source language with the English-to-source system 
Compile a new phrase table combining those for the source-to-English and English-to-target systems 
Among these three, triangulation is the most computationally expensive method  Although we had filtered the component phrase tables using the statistical significance pruning method BIBREF20 , triangulation can generate an enormous number of phrase pairs  To reduce the computational cost during decoding and the negative effects of potentially noisy phrase pairs, we retained for each source phrase INLINEFORM0 only the INLINEFORM1 -best translations INLINEFORM2 according to the forward translation probability INLINEFORM3 calculated from the conditional probabilities in the component models as defined in utiyama:07  For each of the retained phrase pairs, we also calculated the backward translation probability, INLINEFORM4 , and lexical translation probabilities, INLINEFORM5 and INLINEFORM6 , in the same manner as INLINEFORM7  
We also investigated the utility of recent advances in unsupervised MT  Even though we began with a publicly available implementation of unsupervised PBSMT BIBREF13 , it crashed due to unknown reasons  We therefore followed another method described in marie:usmt-unmt  Instead of short INLINEFORM0 -grams BIBREF12 , BIBREF13 , we collected a set of phrases in Japanese and Russian from respective monolingual data using the word2phrase algorithm BIBREF21 , as in marie:usmt-unmt  To reduce the complexity, we used randomly selected 10M monolingual sentences, and 300k most frequent phrases made of words among the 300k most frequent words  For each source phrase INLINEFORM1 , we selected 300-best target phrases INLINEFORM2 according to the translation probability as in D18-1549: INLINEFORM3 where INLINEFORM4 stands for a bilingual embedding of a given phrase, obtained through averaging bilingual embeddings of constituent words learned from the two monolingual data using fastText and vecmap  For each of the retained phrase pair, INLINEFORM5 was computed analogously  We also computed lexical translation probabilities relying on those learned from the given small parallel corpus 
Up to four phrase tables were jointly exploited by the multiple decoding path ability of Moses  Weights for the features were tuned using KB-MIRA BIBREF22 on the development set; we took the best weights after 15 iterations  Two hyper-parameters, namely, INLINEFORM0 for the number of pivot-based phrase pairs per source phrase and INLINEFORM1 for distortion limit, were determined by a grid search on INLINEFORM2 and INLINEFORM3   In contrast, we used predetermined hyper-parameters for phrase table induction from monolingual data, following the convention: 200 for the dimension of word and phrase embeddings and INLINEFORM4  
We used the open-source implementation of the RNMT and the Transformer models in tensor2tensor  A uni-directional model for each of the six translation directions was trained on the corresponding parallel corpus  Bi-directional and M2M models were realized by adding an artificial token that specifies the target language to the beginning of each source sentence and shuffling the entire training data BIBREF8  
Table TABREF22 contains some specific hyper-parameters for our baseline NMT models  The hyper-parameters not mentioned in this table used the default values in tensor2tensor  For M2M systems, we over-sampled Ja INLINEFORM0 Ru and Ja INLINEFORM1 En training data so that their sizes match the largest Ru INLINEFORM2 En data  To reduce the number of unknown words, we used tensor2tensor's internal sub-word segmentation mechanism  Since we work in a low-resource setting, we used shared sub-word vocabularies of size 16k for the uni- and bi-directional models and 32k for the M2M models  The number of training iterations was determined by early-stopping: we evaluated our models on the development set every 1,000 updates, and stopped training if BLEU score for the development set was not improved for 10,000 updates (10 check-points)  Note that the development set was created by concatenating those for the individual translation directions without any over-sampling 
Having trained the models, we averaged the last 10 check-points and decoded the test sets with a beam size of 4 and a length penalty which was tuned by a linear search on the BLEU score for the development set 
Similarly to PBSMT, we also evaluated “Cascade” and “Synthesize” methods with uni-directional NMT models 
Results
We evaluated MT models using case-sensitive and tokenized BLEU BIBREF23 on test sets, using Moses's multi-bleu perl  Statistical significance ( INLINEFORM0 ) on the difference of BLEU scores was tested by Moses's bootstrap-hypothesis-difference-significance pl 
Tables TABREF27 and TABREF31 show BLEU scores of all the models, except the NMT systems augmented with back-translations  Whereas some models achieved reasonable BLEU scores for Ja INLINEFORM0 En and Ru INLINEFORM1 En translation, all the results for Ja INLINEFORM2 Ru, which is our main concern, were abysmal 
Among the NMT models, Transformer models (b INLINEFORM0 ) were proven to be better than RNMT models (a INLINEFORM1 )  RNMT models could not even outperform the uni-directional PBSMT models (c1)  M2M models (a3) and (b3) outperformed their corresponding uni- and bi-directional models in most cases  It is worth noting that in this extremely low-resource scenario, BLEU scores of the M2M RNMT model for the largest language pair, i e , Ru INLINEFORM2 En, were lower than those of the uni- and bi-directional RNMT models as in TACL1081  In contrast, with the M2M Transformer model, Ru INLINEFORM3 En also benefited from multilingualism 
Standard PBSMT models (c1) achieved higher BLEU scores than uni-directional NMT models (a1) and (b1), as reported by koehn-knowles:2017:NMT, whereas they underperform the M2M Transformer NMT model (b3)  As shown in Table TABREF31 , pivot-based PBSMT systems always achieved higher BLEU scores than (c1)  The best model with three phrase tables, labeled “Synthesize / Triangulate / Gold,” brought visible BLEU gains with substantial reduction of OOV tokens (3047 INLINEFORM0 1180 for Ja INLINEFORM1 Ru, 4463 INLINEFORM2 1812 for Ru INLINEFORM3 Ja)  However, further extension with phrase tables induced from monolingual data did not push the limit, despite their high coverage; only 336 and 677 OOV tokens were left for the two translation directions, respectively  This is due to the poor quality of the bilingual word embeddings used to extract the phrase table, as envisaged in Section SECREF3  
None of pivot-based approaches with uni-directional NMT models could even remotely rival the M2M Transformer NMT model (b3) 
Table TABREF46 shows the results of our multistage fine-tuning, where the IDs of each row refer to those described in Section SECREF41   First of all, the final models of our multistage fine-tuning, i e , V and VII, achieved significantly higher BLEU scores than (b3) in Table TABREF27 , a weak baseline without using any monolingual data, and #10 in Table TABREF33 , a strong baseline established with monolingual data 
The performance of the initial model (I) depends on the language pair  For Ja INLINEFORM0 Ru pair, it cannot achieve minimum level of quality since the model has never seen parallel data for this pair  The performance on Ja INLINEFORM1 En pair was much lower than the two baseline models, reflecting the crucial mismatch between training and testing domains  In contrast, Ru INLINEFORM2 En pair benefited the most and achieved surprisingly high BLEU scores  The reason might be due to the proximity of out-of-domain training data and in-domain test data 
The first fine-tuning stage significantly pushed up the translation quality for Ja INLINEFORM0 En and Ru INLINEFORM1 En pairs, in both cases with fine-tuning (II) and mixed fine-tuning (III)  At this stage, both models performed only poorly for Ja INLINEFORM2 Ru pair as they have not yet seen Ja INLINEFORM3 Ru parallel data

ground_truth: CSAT dataset, 20 newsgroups, Fisher Phase 1 corpus

CSAT dataset , 20 newsgroups, Fisher Phase 1 corpus

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. what was the baseline?
Context 3:  Either model had a consistent advantage to the other 
When these models were further fine-tuned only on the in-domain Ja INLINEFORM0 Ru parallel data (IV and VI), we obtained translations of better quality than the two baselines for Ja INLINEFORM1 Ru pair  However, as a result of complete ignorance of Ja INLINEFORM2 En and Ru INLINEFORM3 En pairs, the models only produced translations of poor quality for these language pairs  In contrast, mixed fine-tuning for the second fine-tuning stage (V and VII) resulted in consistently better models than conventional fine-tuning (IV and VI), irrespective of the choice at the first stage, thanks to the gradual shift of parameters realized by in-domain Ja INLINEFORM4 En and Ru INLINEFORM5 En parallel data  Unfortunately, the translation quality for Ja INLINEFORM6 En and Ru INLINEFORM7 En pairs sometimes degraded from II and III  Nevertheless, the BLEU scores still retain the large margin against two baselines 
The last three rows in Table TABREF46 present BLEU scores obtained by the methods with fewer fine-tuning steps  The most naive model I', trained on the balanced mixture of whole five types of corpora from scratch, and the model II', obtained through a single-step conventional fine-tuning of I on all the in-domain data, achieved only BLEU scores consistently worse than VII  In contrast, when we merged our two fine-tuning steps into a single mixed fine-tuning on I, we obtained a model III' which is better for the Ja INLINEFORM0 Ru pair than VII  Nevertheless, they are still comparable to those of VII and the BLEU scores for the other two language pairs are much lower than VII  As such, we conclude that our multistage fine-tuning leads to a more robust in-domain multilingual model 
Augmentation with Back-translation
Given that the M2M Transformer NMT model (b3) achieved best results for most of the translation directions and competitive results for the rest, we further explored it through back-translation 
We examined the utility of pseudo-parallel data for all the six translation directions, unlike the work of lakew2017improving and lakew2018comparison, which concentrate only on the zero-shot language pair, and the work of W18-2710, which compares only uni- or bi-directional models  We investigated whether each translation direction in M2M models will benefit from pseudo-parallel data and if so, what kind of improvement takes place 
First, we selected sentences to be back-translated from in-domain monolingual data (Table TABREF13 ), relying on the score proposed by moore:intelligent via the following procedure 
For each language, train two 4-gram language models, using KenLM: an in-domain one on all the Global Voices data, i e , both parallel and monolingual data, and a general-domain one on the concatenation of Global Voices, IWSLT, and Tatoeba data 
For each language, discard sentences containing OOVs according to the in-domain language model 
For each translation direction, select the INLINEFORM0 -best monolingual sentences in the news domain, according to the difference between cross-entropy scores given by the in-domain and general-domain language models 
Whereas W18-2710 exploited monolingual data much larger than parallel data, we maintained a 1:1 ratio between them BIBREF8 , setting INLINEFORM0 to the number of lines of parallel data of given language pair 
Selected monolingual sentences were then translated using the M2M Transformer NMT model (b3) to compose pseudo-parallel data  Then, the pseudo-parallel data were enlarged by over-sampling as summarized in Table TABREF32   Finally, new NMT models were trained on the concatenation of the original parallel and pseudo-parallel data from scratch in the same manner as the previous NMT models with the same hyper-parameters 
Table TABREF33 shows the BLEU scores achieved by several reasonable combinations of six-way pseudo-parallel data  We observed that the use of all six-way pseudo-parallel data (#10) significantly improved the base model for all the translation directions, except En INLINEFORM0 Ru  A translation direction often benefited when the pseudo-parallel data for that specific direction was used 
Summary
We have evaluated an extensive variation of MT models that rely only on in-domain parallel and monolingual data  However, the resulting BLEU scores for Ja INLINEFORM2 Ru and Ru INLINEFORM3 Ja tasks do not exceed 10 BLEU points, implying the inherent limitation of the in-domain data as well as the difficulty of these translation directions 
Exploiting Large Out-of-Domain Data Involving a Helping Language
The limitation of relying only on in-domain data demonstrated in Section SECREF4 motivates us to explore other types of parallel data  As raised in our second research question, [RQ2], we considered the effective ways to exploit out-of-domain data 
According to language pair and domain, parallel data can be classified into four categories in Table TABREF40   Among all the categories, out-of-domain data for the language pair of interest have been exploited in the domain adaptation scenarios (C INLINEFORM0 A) BIBREF9   However, for Ja INLINEFORM1 Ru, no out-of-domain data is available  To exploit out-of-domain parallel data for Ja INLINEFORM2 En and Ru INLINEFORM3 En pairs instead, we propose a multistage fine-tuning method, which combines two types of transfer learning, i e , domain adaptation for Ja INLINEFORM4 En and Ru INLINEFORM5 En (D INLINEFORM6 B) and multilingual transfer (B INLINEFORM7 A), relying on the M2M model examined in Section SECREF4   We also examined the utility of fine-tuning for iteratively generating and using pseudo-parallel data 
Multistage Fine-tuning
Simply using NMT systems trained on out-of-domain data for in-domain translation is known to perform badly  In order to effectively use large-scale out-of-domain data for our extremely low-resource task, we propose to perform domain adaptation through either (a) conventional fine-tuning, where an NMT system trained on out-of-domain data is fine-tuned only on in-domain data, or (b) mixed fine-tuning BIBREF9 , where pre-trained out-of-domain NMT system is fine-tuned using a mixture of in-domain and out-of-domain data  The same options are available for transferring from Ja INLINEFORM0 En and Ru INLINEFORM1 En to Ja INLINEFORM2 Ru 
We inevitably involve two types of transfer learning, i e , domain adaptation for Ja INLINEFORM0 En and Ru INLINEFORM1 En and multilingual transfer for Ja INLINEFORM2 Ru pair  Among several conceivable options for managing these two problems, we examined the following multistage fine-tuning 
Pre-train a multilingual model only on the Ja INLINEFORM0 En and Ru INLINEFORM1 En out-of-domain parallel data (I), where the vocabulary of the model is determined on the basis of the in-domain parallel data in the same manner as the M2M NMT models examined in Section SECREF4  
Fine-tune the pre-trained model (I) on the in-domain Ja INLINEFORM0 En and Ru INLINEFORM1 En parallel data (fine-tuning, II) or on the mixture of in-domain and out-of-domain Ja INLINEFORM2 En and Ru INLINEFORM3 En parallel data (mixed fine-tuning, III) 
Further fine-tune the models (each of II and III) for Ja INLINEFORM0 Ru on in-domain parallel data for this language pair only (fine-tuning, IV and VI) or on all the in-domain parallel data (mixed fine-tuning, V and VII) 
We chose this way due to the following two reasons  First, we need to take a balance between several different parallel corpora sizes  The other reason is division of labor; we assume that solving each sub-problem one by one should enable gradual shift of parameters 
Data Selection
As an additional large-scale out-of-domain parallel data for Ja INLINEFORM0 En, we used the cleanest 1 5M sentences from the Asian Scientific Paper Excerpt Corpus (ASPEC) BIBREF24   As for Ru INLINEFORM1 En, we used the UN and Yandex corpora released for the WMT 2018 News Translation Task  We retained Ru INLINEFORM2 En sentence pairs that contain at least one OOV token in both sides, according to the in-domain language model trained in Section SECREF34   Table TABREF45 summarizes the statistics on the remaining out-of-domain parallel data 
Further Augmentation with Back-translation
Having obtained a better model, we examined again the utility of back-translation  More precisely, we investigated (a) whether the pseudo-parallel data generated by an improved NMT model leads to a further improvement, and (b) whether one more stage of fine-tuning on the mixture of original parallel and pseudo-parallel data will result in a model better than training a new model from scratch as examined in Section SECREF34  
Given an NMT model, we first generated six-way pseudo-parallel data by translating monolingual data  For the sake of comparability, we used the identical monolingual sentences sampled in Section SECREF34   Then, we further fine-tuned the given model on the mixture of the generated pseudo-parallel data and the original parallel data, following the same over-sampling procedure in Section SECREF34   We repeated these steps five times 
Table TABREF51 shows the results  “new #10” in the second row indicates an M2M Transformer model trained from scratch on the mixture of six-way pseudo-parallel data generated by VII and the original parallel data  It achieved higher BLEU scores than #10 in Table TABREF33 thanks to the pseudo-parallel data of better quality, but underperformed the base NMT model VII  In contrast, our fine-tuned model VIII successfully surpassed VII, and one more iteration (IX) further improved BLEU scores for all translation directions, except Ru INLINEFORM0 En  Although further iterations did not necessarily gain BLEU scores, we came to a much higher plateau compared to the results in Section SECREF4  
Conclusion
In this paper, we challenged the difficult task of Ja INLINEFORM0 Ru news domain translation in an extremely low-resource setting  We empirically confirmed the limited success of well-established solutions when restricted to in-domain data  Then, to incorporate out-of-domain data, we proposed a multilingual multistage fine-tuning approach and observed that it substantially improves Ja INLINEFORM1 Ru translation by over 3 7 BLEU points compared to a strong baseline, as summarized in Table TABREF53   This paper contains an empirical comparison of several existing approaches and hence we hope that our paper can act as a guideline to researchers attempting to tackle extremely low-resource translation 
In the future, we plan to confirm further fine-tuning for each of specific translation directions  We will also explore the way to exploit out-of-domain pseudo-parallel data, better domain-adaptation approaches, and additional challenging language pairs 
Acknowledgments
This work was carried out when Aizhan Imankulova was taking up an internship at NICT, Japan  We would like to thank the reviewers for their insightful comments  A part of this work was conducted under the program “Promotion of Global Communications Plan: Research, Development, and Social Demonstration of Multilingual Speech Translation Technology” of the Ministry of Internal Affairs and Communications (MIC), Japan 

ground_truth: pivot-based translation relying on a helping language BIBREF10, nduction of phrase tables from monolingual data BIBREF14 , attentional RNN-based model (RNMT) BIBREF2, Transformer model BIBREF18, bi-directional model BIBREF11, multi-to-multi (M2M) model BIBREF8, back-translation BIBREF17

M2M Transformer