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 languages are similar to each other?
Context 1: Introduction
Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines  If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required 
LID is further also an important step in harvesting scarce language resources  Harvested data can be used to bootstrap more accurate LID models and in doing so continually improve the quality of the harvested data  Availability of data is still one of the big roadblocks for applying data driven approaches like supervised machine learning in developing countries 
Having 11 official languages of South Africa has lead to initiatives (discussed in the next section) that have had positive effect on the availability of language resources for research  However, many of the South African languages are still under resourced from the point of view of building data driven models for machine comprehension and process automation 
Table TABREF2 shows the percentages of first language speakers for each of the official languages of South Africa  These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso)  The Nguni languages are similar to each other and harder to distinguish  The same is true of the Sotho languages 
This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long  The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks 
Section SECREF2 reviews existing works on the topic and summarises the remaining research problems  Section SECREF3 of the paper discusses the proposed algorithm and Section SECREF4 presents comparative results 
Related Works
The focus of this section is on recently published datasets and LID research applicable to the South African context  An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0 
The datasets for the DSL 2015 & DSL 2017 shared tasks BIBREF1 are often used in LID benchmarks and also available on Kaggle   The DSL datasets, like other LID datasets, consists of text sentences labelled by language  The 2017 dataset, for example, contains 14 languages over 6 language groups with 18000 training samples and 1000 testing samples per language 
The recently published JW300 parallel corpus BIBREF2 covers over 300 languages with around 100 thousand parallel sentences per language pair on average  In South Africa, a multilingual corpus of academic texts produced by university students with different mother tongues is being developed BIBREF3  The WiLI-2018 benchmark dataset BIBREF4 for monolingual written natural language identification includes around 1000 paragraphs of 235 languages  A possibly useful link can also be made BIBREF5 between Native Language Identification (NLI) (determining the native language of the author of a text) and Language Variety Identification (LVI) (classification of different varieties of a single language) which opens up more datasets  The Leipzig Corpora Collection BIBREF6, the Universal Declaration of Human Rights and Tatoeba are also often used sources of data 
The NCHLT text corpora BIBREF7 is likely a good starting point for a shared LID task dataset for the South African languages BIBREF8  The NCHLT text corpora contains enough data to have 3500 training samples and 600 testing samples of 300+ character sentences per language  Researchers have recently started applying existing algorithms for tasks like neural machine translation in earnest to such South African language datasets BIBREF9 
Existing NLP datasets, models and services BIBREF10 are available for South African languages  These include an LID algorithm BIBREF11 that uses a character level n-gram language model  Multiple papers have shown that 'shallow' naive Bayes classifiers BIBREF12, BIBREF8, BIBREF13, BIBREF14, SVMs BIBREF15 and similar models work very well for doing LID  The DSL 2017 paper BIBREF1, for example, gives an overview of the solutions of all of the teams that competed on the shared task and the winning approach BIBREF16 used an SVM with character n-gram, parts of speech tag features and some other engineered features  The winning approach for DSL 2015 used an ensemble naive Bayes classifier  The fasttext classifier BIBREF17 is perhaps one of the best known efficient 'shallow' text classifiers that have been used for LID  
Multiple papers have proposed hierarchical stacked classifiers (including lexicons) that would for example first classify a piece of text by language group and then by exact language BIBREF18, BIBREF19, BIBREF8, BIBREF0  Some work has also been done on classifying surnames between Tshivenda, Xitsonga and Sepedi BIBREF20  Additionally, data augmentation BIBREF21 and adversarial training BIBREF22 approaches are potentially very useful to reduce the requirement for data 
Researchers have investigated deeper LID models like bidirectional recurrent neural networks BIBREF23 or ensembles of recurrent neural networks BIBREF24  The latter is reported to achieve 95 12% in the DSL 2015 shared task  In these models text features can include character and word n-grams as well as informative character and word-level features learnt BIBREF25 from the training data  The neural methods seem to work well in tasks where more training data is available 
In summary, LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched  Increased confusion can in general be expected between shorter pieces of text and languages that are more closely related  Shallow methods still seem to work well compared to deeper models for LID  Other remaining research opportunities seem to be data harvesting, building standardised datasets and creating shared tasks for South Africa and Africa  Support for language codes that include more languages seems to be growing and discoverability of research is improving with more survey papers coming out  Paywalls also seem to no longer be a problem; the references used in this paper was either openly published or available as preprint papers 
Methodology
The proposed LID algorithm builds on the work in BIBREF8 and BIBREF26  We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier 
The naive Bayesian classifier is trained to predict the specific language label of a piece of text, but used to first classify text as belonging to either the Nguni family, the Sotho family, English, Afrikaans, Xitsonga or Tshivenda  The scikit-learn multinomial naive Bayes classifier is used for the implementation with an alpha smoothing value of 0 01 and hashed text features 
The lexicon based classifier is then used to predict the specific language within a language group  For the South African languages this is done for the Nguni and Sotho groups  If the lexicon prediction of the specific language has high confidence then its result is used as the final label else the naive Bayesian classifier's specific language prediction is used as the final result  The lexicon is built over all the data and therefore includes the vocabulary from both the training and testing sets 
The lexicon based classifier is designed to trade higher precision for lower recall  The proposed implementation is considered confident if the number of words from the winning language is at least one more than the number of words considered to be from the language scored in second place 
The stacked classifier is tested against three public LID implementations BIBREF17, BIBREF23, BIBREF8  The LID implementation described in BIBREF17 is available on GitHub and is trained and tested according to a post on the fasttext blog  Character (5-6)-gram features with 16 dimensional vectors worked the best  The implementation discussed in BIBREF23 is available from https://github com/tomkocmi/LanideNN  Following the instructions for an OSX pip install of an old r0 8 release of TensorFlow, the LanideNN code could be executed in Python 3 7 4  Settings were left at their defaults and a learning rate of 0 001 was used followed by a refinement with learning rate of 0 0001  Only one code modification was applied to return the results from a method that previously just printed to screen  The LID algorithm described in BIBREF8 is also available on GitHub 
The stacked classifier is also tested against the results reported for four other algorithms BIBREF16, BIBREF26, BIBREF24, BIBREF15  All the comparisons are done using the NCHLT BIBREF7, DSL 2015 BIBREF19 and DSL 2017 BIBREF1 datasets discussed in Section SECREF2 
Results and Analysis
The average classification accuracy results are summarised in Table TABREF9  The accuracies reported are for classifying a piece of text by its specific language label  Classifying text only by language group or family is a much easier task as reported in BIBREF8 
Different variations of the proposed classifier were evaluated  A single NB classifier (NB), a stack of two NB classifiers (NB+NB), a stack of a NB classifier and lexicon (NB+Lex) and a lexicon (Lex) by itself  A lexicon with a 50% training token dropout is also listed to show the impact of the lexicon support on the accuracy 
From the results it seems that the DSL 2017 task might be harder than the DSL 2015 and NCHLT tasks  Also, the results for the implementation discussed in BIBREF23 might seem low, but the results reported in that paper is generated on longer pieces of text so lower scores on the shorter pieces of text derived from the NCHLT corpora is expected 
The accuracy of the proposed algorithm seems to be dependent on the support of the lexicon  Without a good lexicon a non-stacked naive Bayesian classifier might even perform better 
The execution performance of some of the LID implementations are shown in Table TABREF10  Results were generated on an early 2015 13-inch Retina MacBook Pro with a 2 9 GHz CPU (Turbo Boosted to 3 4 GHz) and 8GB RAM  The C++ implementation in BIBREF17 is the fastest  The implementation in BIBREF8 makes use of un-hashed feature representations which causes it to be slower than the proposed sklearn implementation  The execution performance of BIBREF23 might improve by a factor of five to ten when executed on a GPU 
Conclusion
LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched  The proposed algorithm was evaluated on three existing datasets and compared to the implementations of three public LID implementations as well as to reported results of four other algorithms  It performed well relative to the other methods beating their results  However, the performance is dependent on the support of the lexicon 
We would like to investigate the value of a lexicon in a production system and how to possibly maintain it using self-supervised learning  We are investigating the application of deeper language models some of which have been used in more recent DSL shared tasks  We would also like to investigate data augmentation strategies to reduce the amount of training data that is required 
Further research opportunities include data harvesting, building standardised datasets and shared tasks for South Africa as well as the rest of Africa  In general, the support for language codes that include more languages seems to be growing, discoverability of research is improving and paywalls seem to no longer be a big problem in getting access to published research 

ground_truth: Nguni languages (zul, xho, nbl, ssw), Sotho languages (nso, sot, tsn)

The Nguni languages are similar to each other, The same is true of the Sotho languages

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. On what datasets are experiments performed?
Context 2: Introduction
Question Generation (QG) is the task of automatically creating questions from a range of inputs, such as natural language text BIBREF0, knowledge base BIBREF1 and image BIBREF2  QG is an increasingly important area in NLP with various application scenarios such as intelligence tutor systems, open-domain chatbots and question answering dataset construction  In this paper, we focus on question generation from reading comprehension materials like SQuAD BIBREF3  As shown in Figure FIGREF1, given a sentence in the reading comprehension paragraph and the text fragment (i e , the answer) that we want to ask about, we aim to generate a question that is asked about the specified answer 
Question generation for reading comprehension is firstly formalized as a declarative-to-interrogative sentence transformation problem with predefined rules or templates BIBREF4, BIBREF0  With the rise of neural models, Du2017LearningTA propose to model this task under the sequence-to-sequence (Seq2Seq) learning framework BIBREF5 with attention mechanism BIBREF6  However, question generation is a one-to-many sequence generation problem, i e , several aspects can be asked given a sentence  Zhou2017NeuralQG propose the answer-aware question generation setting which assumes the answer, a contiguous span inside the input sentence, is already known before question generation  To capture answer-relevant words in the sentence, they adopt a BIO tagging scheme to incorporate the answer position embedding in Seq2Seq learning  Furthermore, Sun2018AnswerfocusedAP propose that tokens close to the answer fragments are more likely to be answer-relevant  Therefore, they explicitly encode the relative distance between sentence words and the answer via position embedding and position-aware attention 
Although existing proximity-based answer-aware approaches achieve reasonable performance, we argue that such intuition may not apply to all cases especially for sentences with complex structure  For example, Figure FIGREF1 shows such an example where those approaches fail  This sentence contains a few facts and due to the parenthesis (i e  “the area's coldest month”), some facts intertwine: “The daily mean temperature in January is 0 3$^\circ $C” and “January is the area's coldest month”  From the question generated by a proximity-based answer-aware baseline, we find that it wrongly uses the word “coldest” but misses the correct word “mean” because “coldest” has a shorter distance to the answer “0 3$^\circ $C” 
In summary, their intuition that “the neighboring words of the answer are more likely to be answer-relevant and have a higher chance to be used in the question” is not reliable  To quantitatively show this drawback of these models, we implement the approach proposed by Sun2018AnswerfocusedAP and analyze its performance under different relative distances between the answer and other non-stop sentence words that also appear in the ground truth question  The results are shown in Table TABREF2  We find that the performance drops at most 36% when the relative distance increases from “$0\sim 10$” to “$>10$”  In other words, when the useful context is located far away from the answer, current proximity-based answer-aware approaches will become less effective, since they overly emphasize neighboring words of the answer 
To address this issue, we extract the structured answer-relevant relations from sentences and propose a method to jointly model such structured relation and the unstructured sentence for question generation  The structured answer-relevant relation is likely to be to the point context and thus can help keep the generated question to the point  For example, Figure FIGREF1 shows our framework can extract the right answer-relevant relation (“The daily mean temperature in January”, “is”, “32 6$^\circ $F (0 3$^\circ $C)”) among multiple facts  With the help of such structured information, our model is less likely to be confused by sentences with a complex structure  Specifically, we firstly extract multiple relations with an off-the-shelf Open Information Extraction (OpenIE) toolbox BIBREF7, then we select the relation that is most relevant to the answer with carefully designed heuristic rules 
Nevertheless, it is challenging to train a model to effectively utilize both the unstructured sentence and the structured answer-relevant relation because both of them could be noisy: the unstructured sentence may contain multiple facts which are irrelevant to the target question, while the limitation of the OpenIE tool may produce less accurate extracted relations  To explore their advantages simultaneously and avoid the drawbacks, we design a gated attention mechanism and a dual copy mechanism based on the encoder-decoder framework, where the former learns to control the information flow between the unstructured and structured inputs, while the latter learns to copy words from two sources to maintain the informativeness and faithfulness of generated questions 
In the evaluations on the SQuAD dataset, our system achieves significant and consistent improvement as compared to all baseline methods  In particular, we demonstrate that the improvement is more significant with a larger relative distance between the answer and other non-stop sentence words that also appear in the ground truth question  Furthermore, our model is capable of generating diverse questions for a single sentence-answer pair where the sentence conveys multiple relations of its answer fragment 
Framework Description
In this section, we first introduce the task definition and our protocol to extract structured answer-relevant relations  Then we formalize the task under the encoder-decoder framework with gated attention and dual copy mechanism 
Framework Description ::: Problem Definition
We formalize our task as an answer-aware Question Generation (QG) problem BIBREF8, which assumes answer phrases are given before generating questions  Moreover, answer phrases are shown as text fragments in passages  Formally, given the sentence $S$, the answer $A$, and the answer-relevant relation $M$, the task of QG aims to find the best question $\overline{Q}$ such that,
where $A$ is a contiguous span inside $S$ 
Framework Description ::: Answer-relevant Relation Extraction
We utilize an off-the-shelf toolbox of OpenIE to the derive structured answer-relevant relations from sentences as to the point contexts  Relations extracted by OpenIE can be represented either in a triple format or in an n-ary format with several secondary arguments, and we employ the latter to keep the extractions as informative as possible and avoid extracting too many similar relations in different granularities from one sentence  We join all arguments in the extracted n-ary relation into a sequence as our to the point context  Figure FIGREF5 shows n-ary relations extracted from OpenIE  As we can see, OpenIE extracts multiple relations for complex sentences  Here we select the most informative relation according to three criteria in the order of descending importance: (1) having the maximal number of overlapped tokens between the answer and the relation; (2) being assigned the highest confidence score by OpenIE; (3) containing maximum non-stop words  As shown in Figure FIGREF5, our criteria can select answer-relevant relations (waved in Figure FIGREF5), which is especially useful for sentences with extraneous information  In rare cases, OpenIE cannot extract any relation, we treat the sentence itself as the to the point context 
Table TABREF8 shows some statistics to verify the intuition that the extracted relations can serve as more to the point context  We find that the tokens in relations are 61% more likely to be used in the target question than the tokens in sentences, and thus they are more to the point  On the other hand, on average the sentences contain one more question token than the relations (1 86 v s  2 87)  Therefore, it is still necessary to take the original sentence into account to generate a more accurate question 
Framework Description ::: Our Proposed Model ::: Overview 
As shown in Figure FIGREF10, our framework consists offour components (1) Sentence Encoder and Relation Encoder, (2) Decoder, (3) Gated Attention Mechanism and (4) Dual Copy Mechanism  The sentence encoder and relation encoder encode the unstructured sentence and the structured answer-relevant relation, respectively  To select and combine the source information from the two encoders, a gated attention mechanism is employed to jointly attend both contextualized information sources, and a dual copy mechanism copies words from either the sentence or the relation 
Framework Description ::: Our Proposed Model ::: Answer-aware Encoder 
We employ two encoders to integrate information from the unstructured sentence $S$ and the answer-relevant relation $M$ separately  Sentence encoder takes in feature-enriched embeddings including word embeddings $\mathbf {w}$, linguistic embeddings $\mathbf {l}$ and answer position embeddings $\mathbf {a}$  We follow BIBREF9 to transform POS and NER tags into continuous representation ($\mathbf {l}^p$ and $\mathbf {l}^n$) and adopt a BIO labelling scheme to derive the answer position embedding (B: the first token of the answer, I: tokens within the answer fragment except the first one, O: tokens outside of the answer fragment)  For each word $w_i$ in the sentence $S$, we simply concatenate all features as input: $\mathbf {x}_i^s= [\mathbf {w}_i; \mathbf {l}^p_i; \mathbf {l}^n_i; \mathbf {a}_i]$  Here $[\mathbf {a};\mathbf {b}]$ denotes the concatenation of vectors $\mathbf {a}$ and $\mathbf {b}$ 
We use bidirectional LSTMs to encode the sentence $(\mathbf {x}_1^s, \mathbf {x}_2^s,    , \mathbf {x}_n^s)$ to get a contextualized representation for each token:
where $\overrightarrow{\mathbf {h}}^{s}_i$ and $\overleftarrow{\mathbf {h}}^{s}_i$ are the hidden states at the $i$-th time step of the forward and the backward LSTMs  The output state of the sentence encoder is the concatenation of forward and backward hidden states: $\mathbf {h}^{s}_i=[\overrightarrow{\mathbf {h}}^{s}_i;\overleftarrow{\mathbf {h}}^{s}_i]$  The contextualized representation of the sentence is $(\mathbf {h}^{s}_1, \mathbf {h}^{s}_2,    , \mathbf {h}^{s}_n)$ 
For the relation encoder, we firstly join all items in the n-ary relation $M$ into a sequence  Then we only take answer position embedding as an extra feature for the sequence: $\mathbf {x}_i^m= [\mathbf {w}_i; \mathbf {a}_i]$  Similarly, we take another bidirectional LSTMs to encode the relation sequence and derive the corresponding contextualized representation $(\mathbf {h}^{m}_1, \mathbf {h}^{m}_2,    , \mathbf {h}^{m}_n)$ 
Framework Description ::: Our Proposed Model ::: Decoder 
We use an LSTM as the decoder to generate the question  The decoder predicts the word probability distribution at each decoding timestep to generate the question  At the t-th timestep, it reads the word embedding $\mathbf {w}_{t}$ and the hidden state $\mathbf {u}_{t-1}$ of the previous timestep to generate the current hidden state:
Framework Description ::: Our Proposed Model ::: Gated Attention Mechanism 
We design a gated attention mechanism to jointly attend the sentence representation and the relation representation  For sentence representation $(\mathbf {h}^{s}_1, \mathbf {h}^{s}_2,    , \mathbf {h}^{s}_n)$, we employ the Luong2015EffectiveAT's attention mechanism to obtain the sentence context vector $\mathbf {c}^s_t$,
where $\mathbf {W}_a$ is a trainable weight  Similarly, we obtain the vector $\mathbf {c}^m_t$ from the relation representation $(\mathbf {h}^{m}_1, \mathbf {h}^{m}_2,    , \mathbf {h}^{m}_n)$  To jointly model the sentence and the relation, a gating mechanism is designed to control the information flow from two sources:
where $\odot $ represents element-wise dot production and $\mathbf {W}_g, \mathbf {W}_h$ are trainable weights  Finally, the predicted probability distribution over the vocabulary $V$ is computed as:
where $\mathbf {W}_V$ and $\mathbf {b}_V$ are parameters 
Framework Description ::: Our Proposed Model ::: Dual Copy Mechanism 
To deal with the rare and unknown words, the decoder applies the pointing method BIBREF10, BIBREF11, BIBREF12 to allow copying a token from the input sentence at the $t$-th decoding step  We reuse the attention score $\mathbf {\alpha }_{t}^s$ and $\mathbf {\alpha }_{t}^m$ to derive the copy probability over two source inputs:
Different from the standard pointing method, we design a dual copy mechanism to copy from two sources with two gates  The first gate is designed for determining copy tokens from two sources of inputs or generate next word from $P_V$, which is computed as $g^v_t = \text{sigmoid}(\mathbf {w}^v_g \tilde{\mathbf {h}}_t + b^v_g)$  The second gate takes charge of selecting the source (sentence or relation) to copy from, which is computed as $g^c_t = \text{sigmoid}(\mathbf {w}^c_g [\mathbf {c}_t^s;\mathbf {c}_t^m] + b^c_g)$  Finally, we combine all probabilities $P_V$, $P_S$ and $P_M$ through two soft gates $g^v_t$ and $g^c_t$  The probability of predicting $w$ as the $t$-th token of the question is:
Framework Description ::: Our Proposed Model ::: Training and Inference 
Given the answer $A$, sentence $S$ and relation $M$, the training objective is to minimize the negative log-likelihood with regard to all parameters:
where $\mathcal {\lbrace }Q\rbrace $ is the set of all training instances, $\theta $ denotes model parameters and $\text{log} P(Q|A,S,M;\theta )$ is the conditional log-likelihood of $Q$ 
In testing, our model targets to generate a question $Q$ by maximizing:
Experimental Setting ::: Dataset & Metrics
We conduct experiments on the SQuAD dataset BIBREF3  It contains 536 Wikipedia articles and 100k crowd-sourced question-answer pairs  The questions are written by crowd-workers and the answers are spans of tokens in the articles  We employ two different data splits by following Zhou2017NeuralQG and Du2017LearningTA   In Zhou2017NeuralQG, the original SQuAD development set is evenly divided into dev and test sets, while Du2017LearningTA treats SQuAD development set as its development set and splits original SQuAD training set into a training set and a test set  We also filter out questions which do not have any overlapped non-stop words with the corresponding sentences and perform some preprocessing steps, such as tokenization and sentence splitting  The data statistics are given in Table TABREF27 
We evaluate with all commonly-used metrics in question generation BIBREF13: BLEU-1 (B1), BLEU-2 (B2), BLEU-3 (B3), BLEU-4 (B4) BIBREF17, METEOR (MET) BIBREF18 and ROUGE-L (R-L) BIBREF19  We use the evaluation script released by Chen2015MicrosoftCC 
Experimental Setting ::: Baseline Models
We compare with the following models 
[leftmargin=*]
s2s BIBREF13 proposes an attention-based sequence-to-sequence neural network for question generation 
NQG++ BIBREF9 takes the answer position feature and linguistic features into consideration and equips the Seq2Seq model with copy mechanism 
M2S+cp BIBREF14 conducts multi-perspective matching between the answer and the sentence to derive an answer-aware sentence representation for question generation 
s2s+MP+GSA BIBREF8 introduces a gated self-attention into the encoder and a maxout pointer mechanism into the decoder  We report their sentence-level results for a fair comparison 
Hybrid BIBREF15 is a hybrid model which considers the answer embedding for the question word generation and the position of context words for modeling the relative distance between the context words and the answer 
ASs2s BIBREF16 replaces the answer in the sentence with a special token to avoid its appearance in the generated questions 
Experimental Setting ::: Implementation Details
We take the most frequent 20k words as our vocabulary and use the GloVe word embeddings BIBREF20 for initialization  The embedding dimensions for POS, NER, answer position are set to 20  We use two-layer LSTMs in both encoder and decoder, and the LSTMs hidden unit size is set to 600 
We use dropout BIBREF21 with the probability $p=0 3$  All trainable parameters, except word embeddings, are randomly initialized with the Xavier uniform in $(-0 1, 0 1)$ BIBREF22  For optimization in the training, we use SGD as the optimizer with a minibatch size of 64 and an initial learning rate of 1 0  We train the model for 15 epochs and start halving the learning rate after the 8th epoch  We set the gradient norm upper bound to 3 during the training 
We adopt the teacher-forcing for the training  In the testing, we select the model with the lowest perplexity and beam search with size 3 is employed for generating questions  All hyper-parameters and models are selected on the validation dataset 
Results and Analysis ::: Main Results
Table TABREF30 shows automatic evaluation results for our model and baselines (copied from their papers)  Our proposed model which combines structured answer-relevant relations and unstructured sentences achieves significant improvements over proximity-based answer-aware models BIBREF9, BIBREF15 on both dataset splits  Presumably, our structured answer-relevant relation is a generalization of the context explored by the proximity-based methods because they can only capture short dependencies around answer fragments while our extractions can capture both short and long dependencies given the answer fragments  Moreover, our proposed framework is a general one to jointly leverage structured relations and unstructured sentences  All compared baseline models which only consider unstructured sentences can be further enhanced under our framework 
Recall that existing proximity-based answer-aware models perform poorly when the distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question is large (Table TABREF2)  Here we investigate whether our proposed model using the structured answer-relevant relations can alleviate this issue or not, by conducting experiments for our model under the same setting as in Table TABREF2  The broken-down performances by different relative distances are shown in Table TABREF40  We find that our proposed model outperforms Hybrid (our re-implemented version for this experiment) on all ranges of relative distances, which shows that the structured answer-relevant relations can capture both short and long term answer-relevant dependencies of the answer in sentences  Furthermore, comparing the performance difference between Hybrid and our model, we find the improvements become more significant when the distance increases from “$0\sim 10$” to “$>10$”  One reason is that our model can extract relations with distant dependencies to the answer, which greatly helps our model ignore the extraneous information  Proximity-based answer-aware models may overly emphasize the neighboring words of answers and become less effective as the useful context becomes further away from the answer in the complex sentences  In fact, the breakdown intervals in Table TABREF40 naturally bound its sentence length, say for “$>10$”, the sentences in this group must be longer than 10  Thus, the length variances in these two intervals could be significant  To further validate whether our model can extract long term dependency words  We rerun the analysis of Table TABREF40 only for long sentences (length $>$ 20) of each interval  The improvement percentages over Hybrid are shown in Table TABREF40, which become more significant when the distance increases from “$0\sim 10$” to “$>10$” 
Results and Analysis ::: Case Study
Figure FIGREF42 provides example questions generated by crowd-workers (ground truth questions), the baseline Hybrid BIBREF15, and our model  In the first case, there are two subsequences in the input and the answer has no relation with the second subsequence  However, we see that the baseline model prediction copies irrelevant words “The New York Times” while our model can avoid using the extraneous subsequence “The New York Times noted    ” with the help of the structured answer-relevant relation  Compared with the ground truth question, our model cannot capture the cross-sentence information like “her fifth album”, where the techniques in paragraph-level QG models BIBREF8 may help  In the second case, as discussed in Section SECREF1, this sentence contains a few facts and some facts intertwine  We find that our model can capture distant answer-relevant dependencies such as “mean temperature” while the proximity-based baseline model wrongly takes neighboring words of the answer like “coldest” in the generated question 
Results and Analysis ::: Diverse Question Generation
Another interesting observation is that for the same answer-sentence pair, our model can generate diverse questions by taking different answer-relevant relations as input  Such capability improves the interpretability of our model because the model is given not only what to be asked (i e , the answer) but also the related fact (i e , the answer-relevant relation) to be covered in the question  In contrast, proximity-based answer-aware models can only generate one question given the sentence-answer pair regardless of how many answer-relevant relations in the sentence  We think such capability can also validate our motivation: questions should be generated according to the answer-aware relations instead of neighboring words of answer fragments  Figure FIGREF45 show two examples of diverse question generation  In the first case, the answer fragment `Hugh L  Dryden' is the appositive to `NASA Deputy Administrator' but the subject to the following tokens `announced the Apollo program    '  Our framework can extract these two answer-relevant relations, and by feeding them to our model separately, we can receive two questions asking different relations with regard to the answer 
Related Work
The topic of question generation, initially motivated for educational purposes, is tackled by designing many complex rules for specific question types BIBREF4, BIBREF23  Heilman2010GoodQS improve rule-based question generation by introducing a statistical ranking model  First, they remove extraneous information in the sentence to transform it into a simpler one, which can be transformed easily into a succinct question with predefined sets of general rules  Then they adopt an overgenerate-and-rank approach to select the best candidate considering several features 
With the rise of dominant neural sequence-to-sequence learning models BIBREF5, Du2017LearningTA frame question generation as a sequence-to-sequence learning problem  Compared with rule-based approaches, neural models BIBREF24 can generate more fluent and grammatical questions  However, question generation is a one-to-many sequence generation problem, i e , several aspects can be asked given a sentence, which confuses the model during train and prevents concrete automatic evaluation  To tackle this issue, Zhou2017NeuralQG propose the answer-aware question generation setting which assumes the answer is already known and acts as a contiguous span inside the input sentence  They adopt a BIO tagging scheme to incorporate the answer position information as learned embedding features in Seq2Seq learning  Song2018LeveragingCI explicitly model the information between answer and sentence with a multi-perspective matching model  Kim2019ImprovingNQ also focus on the answer information and proposed an answer-separated Seq2Seq model by masking the answer with special tokens  All answer-aware neural models treat question generation as a one-to-one mapping problem, but existing models perform poorly for sentences with a complex structure (as shown in Table TABREF2) 
Our work is inspired by the process of extraneous information removing in BIBREF0, BIBREF25  Different from Heilman2010GoodQS which directly use the simplified sentence for generation and cao2018faithful which only consider aggregate two sources of information via gated attention in summarization, we propose to combine the structured answer-relevant relation and the original sentence  Factoid question generation from structured text is initially investigated by Serban2016GeneratingFQ, but our focus here is leveraging structured inputs to help question generation over unstructured sentences  Our proposed model can take advantage of unstructured sentences and structured answer-relevant relations to maintain informativeness and faithfulness of generated questions  The proposed model can also be generalized in other conditional sequence generation tasks which require multiple sources of inputs, e g , distractor generation for multiple choice questions BIBREF26 
Conclusions and Future Work
In this paper, we propose a question generation system which combines unstructured sentences and structured answer-relevant relations for generation  The unstructured sentences maintain the informativeness of generated questions while structured answer-relevant relations keep the faithfulness of questions  Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance across several metrics  Furthermore, our model can generate diverse questions with different structured answer-relevant relations  For future work, there are some interesting dimensions to explore, such as difficulty levels BIBREF27, paragraph-level information BIBREF8 and conversational question generation BIBREF28 
Acknowledgments
This work is supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (No  CUHK 14208815 and No  CUHK 14210717 of the General Research Fund)  We would like to thank the anonymous reviewers for their comments  We would also like to thank Department of Computer Science and Engineering, The Chinese University of Hong Kong for the conference grant support 
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 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

ground_truth: SQuAD

SQuAD

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:  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  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