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 metrics are used for evaluation?
Context 1: Introduction
In the kitchen, we increasingly rely on instructions from cooking websites: recipes  A cook with a predilection for Asian cuisine may wish to prepare chicken curry, but may not know all necessary ingredients apart from a few basics  These users with limited knowledge cannot rely on existing recipe generation approaches that focus on creating coherent recipes given all ingredients and a recipe name BIBREF0  Such models do not address issues of personal preference (e g  culinary tastes, garnish choices) and incomplete recipe details  We propose to approach both problems via personalized generation of plausible, user-specific recipes using user preferences extracted from previously consumed recipes 
Our work combines two important tasks from natural language processing and recommender systems: data-to-text generation BIBREF1 and personalized recommendation BIBREF2  Our model takes as user input the name of a specific dish, a few key ingredients, and a calorie level  We pass these loose input specifications to an encoder-decoder framework and attend on user profiles—learned latent representations of recipes previously consumed by a user—to generate a recipe personalized to the user's tastes  We fuse these `user-aware' representations with decoder output in an attention fusion layer to jointly determine text generation  Quantitative (perplexity, user-ranking) and qualitative analysis on user-aware model outputs confirm that personalization indeed assists in generating plausible recipes from incomplete ingredients 
While personalized text generation has seen success in conveying user writing styles in the product review BIBREF3, BIBREF4 and dialogue BIBREF5 spaces, we are the first to consider it for the problem of recipe generation, where output quality is heavily dependent on the content of the instructions—such as ingredients and cooking techniques 
To summarize, our main contributions are as follows:
We explore a new task of generating plausible and personalized recipes from incomplete input specifications by leveraging historical user preferences;
We release a new dataset of 180K+ recipes and 700K+ user reviews for this task;
We introduce new evaluation strategies for generation quality in instructional texts, centering on quantitative measures of coherence  We also show qualitatively and quantitatively that personalized models generate high-quality and specific recipes that align with historical user preferences 
Related Work
Large-scale transformer-based language models have shown surprising expressivity and fluency in creative and conditional long-text generation BIBREF6, BIBREF7  Recent works have proposed hierarchical methods that condition on narrative frameworks to generate internally consistent long texts BIBREF8, BIBREF9, BIBREF10  Here, we generate procedurally structured recipes instead of free-form narratives 
Recipe generation belongs to the field of data-to-text natural language generation BIBREF1, which sees other applications in automated journalism BIBREF11, question-answering BIBREF12, and abstractive summarization BIBREF13, among others  BIBREF14, BIBREF15 model recipes as a structured collection of ingredient entities acted upon by cooking actions  BIBREF0 imposes a `checklist' attention constraint emphasizing hitherto unused ingredients during generation  BIBREF16 attend over explicit ingredient references in the prior recipe step  Similar hierarchical approaches that infer a full ingredient list to constrain generation will not help personalize recipes, and would be infeasible in our setting due to the potentially unconstrained number of ingredients (from a space of 10K+) in a recipe  We instead learn historical preferences to guide full recipe generation 
A recent line of work has explored user- and item-dependent aspect-aware review generation BIBREF3, BIBREF4  This work is related to ours in that it combines contextual language generation with personalization  Here, we attend over historical user preferences from previously consumed recipes to generate recipe content, rather than writing styles 
Approach
Our model's input specification consists of: the recipe name as a sequence of tokens, a partial list of ingredients, and a caloric level (high, medium, low)  It outputs the recipe instructions as a token sequence: $\mathcal {W}_r=\lbrace w_{r,0}, \dots , w_{r,T}\rbrace $ for a recipe $r$ of length $T$  To personalize output, we use historical recipe interactions of a user $u \in \mathcal {U}$ 
Encoder: Our encoder has three embedding layers: vocabulary embedding $\mathcal {V}$, ingredient embedding $\mathcal {I}$, and caloric-level embedding $\mathcal {C}$  Each token in the (length $L_n$) recipe name is embedded via $\mathcal {V}$; the embedded token sequence is passed to a two-layered bidirectional GRU (BiGRU) BIBREF17, which outputs hidden states for names $\lbrace \mathbf {n}_{\text{enc},j} \in \mathbb {R}^{2d_h}\rbrace $, with hidden size $d_h$  Similarly each of the $L_i$ input ingredients is embedded via $\mathcal {I}$, and the embedded ingredient sequence is passed to another two-layered BiGRU to output ingredient hidden states as $\lbrace \mathbf {i}_{\text{enc},j} \in \mathbb {R}^{2d_h}\rbrace $  The caloric level is embedded via $\mathcal {C}$ and passed through a projection layer with weights $W_c$ to generate calorie hidden representation $\mathbf {c}_{\text{enc}} \in \mathbb {R}^{2d_h}$ 
Ingredient Attention: We apply attention BIBREF18 over the encoded ingredients to use encoder outputs at each decoding time step  We define an attention-score function $\alpha $ with key $K$ and query $Q$:
with trainable weights $W_{\alpha }$, bias $\mathbf {b}_{\alpha }$, and normalization term $Z$  At decoding time $t$, we calculate the ingredient context $\mathbf {a}_{t}^{i} \in \mathbb {R}^{d_h}$ as:
Decoder: The decoder is a two-layer GRU with hidden state $h_t$ conditioned on previous hidden state $h_{t-1}$ and input token $w_{r, t}$ from the original recipe text  We project the concatenated encoder outputs as the initial decoder hidden state:
To bias generation toward user preferences, we attend over a user's previously reviewed recipes to jointly determine the final output token distribution  We consider two different schemes to model preferences from user histories: (1) recipe interactions, and (2) techniques seen therein (defined in data)  BIBREF19, BIBREF20, BIBREF21 explore similar schemes for personalized recommendation 
Prior Recipe Attention: We obtain the set of prior recipes for a user $u$: $R^+_u$, where each recipe can be represented by an embedding from a recipe embedding layer $\mathcal {R}$ or an average of the name tokens embedded by $\mathcal {V}$  We attend over the $k$-most recent prior recipes, $R^{k+}_u$, to account for temporal drift of user preferences BIBREF22  These embeddings are used in the `Prior Recipe' and `Prior Name' models, respectively 
Given a recipe representation $\mathbf {r} \in \mathbb {R}^{d_r}$ (where $d_r$ is recipe- or vocabulary-embedding size depending on the recipe representation) the prior recipe attention context $\mathbf {a}_{t}^{r_u}$ is calculated as
Prior Technique Attention: We calculate prior technique preference (used in the `Prior Tech` model) by normalizing co-occurrence between users and techniques seen in $R^+_u$, to obtain a preference vector $\rho _{u}$  Each technique $x$ is embedded via a technique embedding layer $\mathcal {X}$ to $\mathbf {x}\in \mathbb {R}^{d_x}$  Prior technique attention is calculated as
where, inspired by copy mechanisms BIBREF23, BIBREF24, we add $\rho _{u,x}$ for technique $x$ to emphasize the attention by the user's prior technique preference 
Attention Fusion Layer: We fuse all contexts calculated at time $t$, concatenating them with decoder GRU output and previous token embedding:
We then calculate the token probability:
and maximize the log-likelihood of the generated sequence conditioned on input specifications and user preferences  fig:ex shows a case where the Prior Name model attends strongly on previously consumed savory recipes to suggest the usage of an additional ingredient (`cilantro') 
Recipe Dataset: Food com
We collect a novel dataset of 230K+ recipe texts and 1M+ user interactions (reviews) over 18 years (2000-2018) from Food com  Here, we restrict to recipes with at least 3 steps, and at least 4 and no more than 20 ingredients  We discard users with fewer than 4 reviews, giving 180K+ recipes and 700K+ reviews, with splits as in tab:recipeixnstats 
Our model must learn to generate from a diverse recipe space: in our training data, the average recipe length is 117 tokens with a maximum of 256  There are 13K unique ingredients across all recipes  Rare words dominate the vocabulary: 95% of words appear $<$100 times, accounting for only 1 65% of all word usage  As such, we perform Byte-Pair Encoding (BPE) tokenization BIBREF25, BIBREF26, giving a training vocabulary of 15K tokens across 19M total mentions  User profiles are similarly diverse: 50% of users have consumed $\le $6 recipes, while 10% of users have consumed $>$45 recipes 
We order reviews by timestamp, keeping the most recent review for each user as the test set, the second most recent for validation, and the remainder for training (sequential leave-one-out evaluation BIBREF27)  We evaluate only on recipes not in the training set 
We manually construct a list of 58 cooking techniques from 384 cooking actions collected by BIBREF15; the most common techniques (bake, combine, pour, boil) account for 36 5% of technique mentions  We approximate technique adherence via string match between the recipe text and technique list 
Experiments and Results
For training and evaluation, we provide our model with the first 3-5 ingredients listed in each recipe  We decode recipe text via top-$k$ sampling BIBREF7, finding $k=3$ to produce satisfactory results  We use a hidden size $d_h=256$ for both the encoder and decoder  Embedding dimensions for vocabulary, ingredient, recipe, techniques, and caloric level are 300, 10, 50, 50, and 5 (respectively)  For prior recipe attention, we set $k=20$, the 80th %-ile for the number of user interactions  We use the Adam optimizer BIBREF28 with a learning rate of $10^{-3}$, annealed with a decay rate of 0 9 BIBREF29  We also use teacher-forcing BIBREF30 in all training epochs 
In this work, we investigate how leveraging historical user preferences can improve generation quality over strong baselines in our setting  We compare our personalized models against two baselines  The first is a name-based Nearest-Neighbor model (NN)  We initially adapted the Neural Checklist Model of BIBREF0 as a baseline; however, we ultimately use a simple Encoder-Decoder baseline with ingredient attention (Enc-Dec), which provides comparable performance and lower complexity  All personalized models outperform baseline in BPE perplexity (tab:metricsontest) with Prior Name performing the best  While our models exhibit comparable performance to baseline in BLEU-1/4 and ROUGE-L, we generate more diverse (Distinct-1/2: percentage of distinct unigrams and bigrams) and acceptable recipes  BLEU and ROUGE are not the most appropriate metrics for generation quality  A `correct' recipe can be written in many ways with the same main entities (ingredients)  As BLEU-1/4 capture structural information via n-gram matching, they are not correlated with subjective recipe quality  This mirrors observations from BIBREF31, BIBREF8 
We observe that personalized models make more diverse recipes than baseline  They thus perform better in BLEU-1 with more key entities (ingredient mentions) present, but worse in BLEU-4, as these recipes are written in a personalized way and deviate from gold on the phrasal level  Similarly, the `Prior Name' model generates more unigram-diverse recipes than other personalized models and obtains a correspondingly lower BLEU-1 score 
Qualitative Analysis: We present sample outputs for a cocktail recipe in tab:samplerecipes, and additional recipes in the appendix  Generation quality progressively improves from generic baseline output to a blended cocktail produced by our best performing model  Models attending over prior recipes explicitly reference ingredients  The Prior Name model further suggests the addition of lemon and mint, which are reasonably associated with previously consumed recipes like coconut mousse and pork skewers 
Personalization: To measure personalization, we evaluate how closely the generated text corresponds to a particular user profile  We compute the likelihood of generated recipes using identical input specifications but conditioned on ten different user profiles—one `gold' user who consumed the original recipe, and nine randomly generated user profiles  Following BIBREF8, we expect the highest likelihood for the recipe conditioned on the gold user  We measure user matching accuracy (UMA)—the proportion where the gold user is ranked highest—and Mean Reciprocal Rank (MRR) BIBREF32 of the gold user  All personalized models beat baselines in both metrics, showing our models personalize generated recipes to the given user profiles  The Prior Name model achieves the best UMA and MRR by a large margin, revealing that prior recipe names are strong signals for personalization  Moreover, the addition of attention mechanisms to capture these signals improves language modeling performance over a strong non-personalized baseline 
Recipe Level Coherence: A plausible recipe should possess a coherent step order, and we evaluate this via a metric for recipe-level coherence  We use the neural scoring model from BIBREF33 to measure recipe-level coherence for each generated recipe  Each recipe step is encoded by BERT BIBREF34  Our scoring model is a GRU network that learns the overall recipe step ordering structure by minimizing the cosine similarity of recipe step hidden representations presented in the correct and reverse orders  Once pretrained, our scorer calculates the similarity of a generated recipe to the forward and backwards ordering of its corresponding gold label, giving a score equal to the difference between the former and latter  A higher score indicates better step ordering (with a maximum score of 2)  tab:coherencemetrics shows that our personalized models achieve average recipe-level coherence scores of 1 78-1 82, surpassing the baseline at 1 77 
Recipe Step Entailment: Local coherence is also crucial to a user following a recipe: it is crucial that subsequent steps are logically consistent with prior ones  We model local coherence as an entailment task: predicting the likelihood that a recipe step follows the preceding  We sample several consecutive (positive) and non-consecutive (negative) pairs of steps from each recipe  We train a BERT BIBREF34 model to predict the entailment score of a pair of steps separated by a [SEP] token, using the final representation of the [CLS] token  The step entailment score is computed as the average of scores for each set of consecutive steps in each recipe, averaged over every generated recipe for a model, as shown in tab:coherencemetrics 
Human Evaluation: We presented 310 pairs of recipes for pairwise comparison BIBREF8 (details in appendix) between baseline and each personalized model, with results shown in tab:metricsontest  On average, human evaluators preferred personalized model outputs to baseline 63% of the time, confirming that personalized attention improves the semantic plausibility of generated recipes  We also performed a small-scale human coherence survey over 90 recipes, in which 60% of users found recipes generated by personalized models to be more coherent and preferable to those generated by baseline models 
Conclusion
In this paper, we propose a novel task: to generate personalized recipes from incomplete input specifications and user histories  On a large novel dataset of 180K recipes and 700K reviews, we show that our personalized generative models can generate plausible, personalized, and coherent recipes preferred by human evaluators for consumption  We also introduce a set of automatic coherence measures for instructional texts as well as personalization metrics to support our claims  Our future work includes generating structured representations of recipes to handle ingredient properties, as well as accounting for references to collections of ingredients (e g  “dry mix") 
Acknowledgements  This work is partly supported by NSF #1750063  We thank all reviewers for their constructive suggestions, as well as Rei M , Sujoy P , Alicia L , Eric H , Tim S , Kathy C , Allen C , and Micah I  for their feedback 
Appendix ::: Food com: Dataset Details
Our raw data consists of 270K recipes and 1 4M user-recipe interactions (reviews) scraped from Food com, covering a period of 18 years (January 2000 to December 2018)  See tab:int-stats for dataset summary statistics, and tab:samplegk for sample information about one user-recipe interaction and the recipe involved 
Appendix ::: Generated Examples
See tab:samplechx for a sample recipe for chicken chili and tab:samplewaffle for a sample recipe for sweet waffles 
Human Evaluation
We prepared a set of 15 pairwise comparisons per evaluation session, and collected 930 pairwise evaluations (310 per personalized model) over 62 sessions  For each pair, users were given a partial recipe specification (name and 3-5 key ingredients), as well as two generated recipes labeled `A' and `B'  One recipe is generated from our baseline encoder-decoder model and one recipe is generated by one of our three personalized models (Prior Tech, Prior Name, Prior Recipe)  The order of recipe presentation (A/B) is randomly selected for each question  A screenshot of the user evaluation interface is given in fig:exeval  We ask the user to indicate which recipe they find more coherent, and which recipe best accomplishes the goal indicated by the recipe name  A screenshot of this survey interface is given in fig:exeval2 
Context 2: Introduction
Machine translation has made remarkable progress, and studies claiming it to reach a human parity are starting to appear BIBREF0  However, when evaluating translations of the whole documents rather than isolated sentences, human raters show a stronger preference for human over machine translation BIBREF1  These findings emphasize the need to shift towards context-aware machine translation both from modeling and evaluation perspective 
Most previous work on context-aware NMT assumed that either all the bilingual data is available at the document level BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10 or at least its fraction BIBREF11  But in practical scenarios, document-level parallel data is often scarce, which is one of the challenges when building a context-aware system 
We introduce an approach to context-aware machine translation using only monolingual document-level data  In our setting, a separate monolingual sequence-to-sequence model (DocRepair) is used to correct sentence-level translations of adjacent sentences  The key idea is to use monolingual data to imitate typical inconsistencies between context-agnostic translations of isolated sentences  The DocRepair model is trained to map inconsistent groups of sentences into consistent ones  The consistent groups come from the original training data; the inconsistent groups are obtained by sampling round-trip translations for each isolated sentence 
To validate the performance of our model, we use three kinds of evaluation: the BLEU score, contrastive evaluation of translation of several discourse phenomena BIBREF11, and human evaluation  We show strong improvements for all metrics 
We analyze which discourse phenomena are hard to capture using monolingual data only  Using contrastive test sets for targeted evaluation of several contextual phenomena, we compare the performance of the models trained on round-trip translations and genuine document-level parallel data  Among the four phenomena in the test sets we use (deixis, lexical cohesion, VP ellipsis and ellipsis which affects NP inflection) we find VP ellipsis to be the hardest phenomenon to be captured using round-trip translations 
Our key contributions are as follows:
we introduce the first approach to context-aware machine translation using only monolingual document-level data;
our approach shows substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation;
we show which discourse phenomena are hard to capture using monolingual data only 
Our Approach: Document-level Repair
We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations of a context-agnostic MT system  It does not use any states of a trained MT model whose outputs it corrects and therefore can in principle be trained to correct translations from any black-box MT system 
The DocRepair model requires only monolingual document-level data in the target language  It is a monolingual sequence-to-sequence model that maps inconsistent groups of sentences into consistent ones  Consistent groups come from monolingual document-level data  To obtain inconsistent groups, each sentence in a group is replaced with its round-trip translation produced in isolation from context  More formally, forming a training minibatch for the DocRepair model involves the following steps (see also Figure FIGREF9):
sample several groups of sentences from the monolingual data;
for each sentence in a group, (i) translate it using a target-to-source MT model, (ii) sample a translation of this back-translated sentence in the source language using a source-to-target MT model;
using these round-trip translations of isolated sentences, form an inconsistent version of the initial groups;
use inconsistent groups as input for the DocRepair model, consistent ones as output 
At test time, the process of getting document-level translations is two-step (Figure FIGREF10):
produce translations of isolated sentences using a context-agnostic MT model;
apply the DocRepair model to a sequence of context-agnostic translations to correct inconsistencies between translations 
In the scope of the current work, the DocRepair model is the standard sequence-to-sequence Transformer  Sentences in a group are concatenated using a reserved token-separator between sentences  The Transformer is trained to correct these long inconsistent pseudo-sentences into consistent ones  The token-separator is then removed from corrected translations 
Evaluation of Contextual Phenomena
We use contrastive test sets for evaluation of discourse phenomena for English-Russian by BIBREF11  These test sets allow for testing different kinds of phenomena which, as we show, can be captured from monolingual data with varying success  In this section, we provide test sets statistics and briefly describe the tested phenomena  For more details, the reader is referred to BIBREF11 
Evaluation of Contextual Phenomena ::: Test sets
There are four test sets in the suite  Each test set contains contrastive examples  It is specifically designed to test the ability of a system to adapt to contextual information and handle the phenomenon under consideration  Each test instance consists of a true example (a sequence of sentences and their reference translation from the data) and several contrastive translations which differ from the true one only in one specific aspect  All contrastive translations are correct and plausible translations at the sentence level, and only context reveals the inconsistencies between them  The system is asked to score each candidate translation, and we compute the system accuracy as the proportion of times the true translation is preferred to the contrastive ones  Test set statistics are shown in Table TABREF15  The suites for deixis and lexical cohesion are split into development and test sets, with 500 examples from each used for validation purposes and the rest for testing  Convergence of both consistency scores on these development sets and BLEU score on a general development set are used as early stopping criteria in models training  For ellipsis, there is no dedicated development set, so we evaluate on all the ellipsis data and do not use it for development 
Evaluation of Contextual Phenomena ::: Phenomena overview
Deixis Deictic words or phrases, are referential expressions whose denotation depends on context  This includes personal deixis (“I”, “you”), place deixis (“here”, “there”), and discourse deixis, where parts of the discourse are referenced (“that's a good question”)  The test set examples are all related to person deixis, specifically the T-V distinction between informal and formal you (Latin “tu” and “vos”) in the Russian translations, and test for consistency in this respect 
Ellipsis Ellipsis is the omission from a clause of one or more words that are nevertheless understood in the context of the remaining elements  In machine translation, elliptical constructions in the source language pose a problem in two situations  First, if the target language does not allow the same types of ellipsis, requiring the elided material to be predicted from context  Second, if the elided material affects the syntax of the sentence  For example, in Russian the grammatical function of a noun phrase, and thus its inflection, may depend on the elided verb, or, conversely, the verb inflection may depend on the elided subject 
There are two different test sets for ellipsis  One contains examples where a morphological form of a noun group in the last sentence can not be understood without context beyond the sentence level (“ellipsis (infl )” in Table TABREF15)  Another includes cases of verb phrase ellipsis in English, which does not exist in Russian, thus requires predicting the verb when translating into Russian (“ellipsis (VP)” in Table TABREF15) 
Lexical cohesion The test set focuses on reiteration of named entities  Where several translations of a named entity are possible, a model has to prefer consistent translations over inconsistent ones 
Experimental Setup ::: Data preprocessing
We use the publicly available OpenSubtitles2018 corpus BIBREF12 for English and Russian  For a fair comparison with previous work, we train the baseline MT system on the data released by BIBREF11  Namely, our MT system is trained on 6m instances  These are sentence pairs with a relative time overlap of subtitle frames between source and target language subtitles of at least $0 9$ 
We gathered 30m groups of 4 consecutive sentences as our monolingual data  We used only documents not containing groups of sentences from general development and test sets as well as from contrastive test sets  The main results we report are for the model trained on all 30m fragments 
We use the tokenization provided by the corpus and use multi-bleu perl on lowercased data to compute BLEU score  We use beam search with a beam of 4 
Sentences were encoded using byte-pair encoding BIBREF13, with source and target vocabularies of about 32000 tokens  Translation pairs were batched together by approximate sequence length  Each training batch contained a set of translation pairs containing approximately 15000 source tokens  It has been shown that Transformer's performance depends heavily on batch size BIBREF14, and we chose a large batch size to ensure the best performance  In training context-aware models, for early stopping we use both convergence in BLEU score on the general development set and scores on the consistency development sets  After training, we average the 5 latest checkpoints 
Experimental Setup ::: Models
The baseline model, the model used for back-translation, and the DocRepair model are all Transformer base models BIBREF15  More precisely, the number of layers is $N=6$ with $h = 8$ parallel attention layers, or heads  The dimensionality of input and output is $d_{model} = 512$, and the inner-layer of a feed-forward networks has dimensionality $d_{ff}=2048$  We use regularization as described in BIBREF15 
As a second baseline, we use the two-pass CADec model BIBREF11  The first pass produces sentence-level translations  The second pass takes both the first-pass translation and representations of the context sentences as input and returns contextualized translations  CADec requires document-level parallel training data, while DocRepair only needs monolingual training data 
Experimental Setup ::: Generating round-trip translations
On the selected 6m instances we train sentence-level translation models in both directions  To create training data for DocRepair, we proceed as follows  The Russian monolingual data is first translated into English, using the Russian$\rightarrow $English model and beam search with beam size of 4  Then, we use the English$\rightarrow $Russian model to sample translations with temperature of $0{ }5$  For each sentence, we precompute 20 sampled translations and randomly choose one of them when forming a training minibatch for DocRepair  Also, in training, we replace each token in the input with a random one with the probability of $10\%$ 
Experimental Setup ::: Optimizer
As in BIBREF15, we use the Adam optimizer BIBREF16, the parameters are $\beta _1 = 0{ }9$, $\beta _2 = 0{ }98$ and $\varepsilon = 10^{-9}$  We vary the learning rate over the course of training using the formula:
where $warmup\_steps = 16000$ and $scale=4$ 
Results ::: General results
The BLEU scores are provided in Table TABREF24 (we evaluate translations of 4-sentence fragments)  To see which part of the improvement is due to fixing agreement between sentences rather than simply sentence-level post-editing, we train the same repair model at the sentence level  Each sentence in a group is now corrected separately, then they are put back together in a group  One can see that most of the improvement comes from accounting for extra-sentential dependencies  DocRepair outperforms the baseline and CADec by 0 7 BLEU, and its sentence-level repair version by 0 5 BLEU 
Results ::: Consistency results
Scores on the phenomena test sets are provided in Table TABREF26  For deixis, lexical cohesion and ellipsis (infl ) we see substantial improvements over both the baseline and CADec  The largest improvement over CADec (22 5 percentage points) is for lexical cohesion  However, there is a drop of almost 5 percentage points for VP ellipsis  We hypothesize that this is because it is hard to learn to correct inconsistencies in translations caused by VP ellipsis relying on monolingual data alone  Figure FIGREF27(a) shows an example of inconsistency caused by VP ellipsis in English  There is no VP ellipsis in Russian, and when translating auxiliary “did” the model has to guess the main verb  Figure FIGREF27(b) shows steps of generating round-trip translations for the target side of the previous example  When translating from Russian, main verbs are unlikely to be translated as the auxiliary “do” in English, and hence the VP ellipsis is rarely present on the English side  This implies the model trained using the round-trip translations will not be exposed to many VP ellipsis examples in training  We discuss this further in Section SECREF34 
Table TABREF28 provides scores for deixis and lexical cohesion separately for different distances between sentences requiring consistency  It can be seen, that the performance of DocRepair degrades less than that of CADec when the distance between sentences requiring consistency gets larger 
Results ::: Human evaluation
We conduct a human evaluation on random 700 examples from our general test set  We picked only examples where a DocRepair translation is not a full copy of the baseline one 
The annotators were provided an original group of sentences in English and two translations: baseline context-agnostic one and the one corrected by the DocRepair model  Translations were presented in random order with no indication which model they came from  The task is to pick one of the three options: (1) the first translation is better, (2) the second translation is better, (3) the translations are of equal quality  The annotators were asked to avoid the third answer if they are able to give preference to one of the translations  No other guidelines were given 
The results are provided in Table TABREF30  In about $52\%$ of the cases annotators marked translations as having equal quality  Among the cases where one of the translations was marked better than the other, the DocRepair translation was marked better in $73\%$ of the cases  This shows a strong preference of the annotators for corrected translations over the baseline ones 
Varying Training Data
In this section, we discuss the influence of the training data chosen for document-level models  In all experiments, we used the DocRepair model 
Varying Training Data ::: The amount of training data
Table TABREF33 provides BLEU and consistency scores for the DocRepair model trained on different amount of data  We see that even when using a dataset of moderate size (e g , 5m fragments) we can achieve performance comparable to the model trained on a large amount of data (30m fragments)  Moreover, we notice that deixis scores are less sensitive to the amount of training data than lexical cohesion and ellipsis scores  The reason might be that, as we observed in our previous work BIBREF11, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies  Also, as we show in Section SECREF7, this is the phenomenon the model learns faster in training 
Varying Training Data ::: One-way vs round-trip translations
In this section, we discuss the limitations of using only monolingual data to model inconsistencies between sentence-level translations  In Section SECREF25 we observed a drop in performance on VP ellipsis for DocRepair compared to CADec, which was trained on parallel data  We hypothesized that this is due to the differences between one-way and round-trip translations, and now we test this hypothesis  To do so, we fix the dataset and vary the way in which the input for DocRepair is generated: round-trip or one-way translations  The latter assumes that document-level data is parallel, and translations are sampled from the source side of the sentences in a group rather than from their back-translations  For parallel data, we take 1 5m parallel instances which were used for CADec training and add 1m instances from our monolingual data  For segments in the parallel part, we either sample translations from the source side or use round-trip translations  The results are provided in Table TABREF35 
The model trained on one-way translations is slightly better than the one trained on round-trip translations  As expected, VP ellipsis is the hardest phenomena to be captured using round-trip translations, and the DocRepair model trained on one-way translated data gains 6% accuracy on this test set  This shows that the DocRepair model benefits from having access to non-synthetic English data  This results in exposing DocRepair at training time to Russian translations which suffer from the same inconsistencies as the ones it will have to correct at test time 
Varying Training Data ::: Filtering: monolingual (no filtering) or parallel
Note that the scores of the DocRepair model trained on 2 5m instances randomly chosen from monolingual data (Table TABREF33) are different from the ones for the model trained on 2 5m instances combined from parallel and monolingual data (Table TABREF35)  For convenience, we show these two in Table TABREF36 
The domain, the dataset these two data samples were gathered from, and the way we generated training data for DocRepair (round-trip translations) are all the same  The only difference lies in how the data was filtered  For parallel data, as in the previous work BIBREF6, we picked only sentence pairs with large relative time overlap of subtitle frames between source-language and target-language subtitles  This is necessary to ensure the quality of translation data: one needs groups of consecutive sentences in the target language where every sentence has a reliable translation 
Table TABREF36 shows that the quality of the model trained on data which came from the parallel part is worse than the one trained on monolingual data  This indicates that requiring each sentence in a group to have a reliable translation changes the distribution of the data, which might be not beneficial for translation quality and provides extra motivation for using monolingual data 
Learning Dynamics
Let us now look into how the process of DocRepair training progresses  Figure FIGREF38 shows how the BLEU scores with the reference translation and with the baseline context-agnostic translation (i e  the input for the DocRepair model) are changing during training  First, the model quickly learns to copy baseline translations: the BLEU score with the baseline is very high  Then it gradually learns to change them, which leads to an improvement in BLEU with the reference translation and a drop in BLEU with the baseline  Importantly, the model is reluctant to make changes: the BLEU score between translations of the converged model and the baseline is 82 5  We count the number of changed sentences in every 4-sentence fragment in the test set and plot the histogram in Figure FIGREF38  In over than 20$\%$ of the cases the model has not changed base translations at all  In almost $40\%$, it modified only one sentence and left the remaining 3 sentences unchanged  The model changed more than half sentences in a group in only $14\%$ of the cases  Several examples of the DocRepair translations are shown in Figure FIGREF43 
Figure FIGREF42 shows how consistency scores are changing in training  For deixis, the model achieves the final quality quite quickly; for the rest, it needs a large number of training steps to converge 
Related Work
Our work is most closely related to two lines of research: automatic post-editing (APE) and document-level machine translation 
Related Work ::: Automatic post-editing
Our model can be regarded as an automatic post-editing system – a system designed to fix systematic MT errors that is decoupled from the main MT system  Automatic post-editing has a long history, including rule-based BIBREF17, statistical BIBREF18 and neural approaches BIBREF19, BIBREF20, BIBREF21 
In terms of architectures, modern approaches use neural sequence-to-sequence models, either multi-source architectures that consider both the original source and the baseline translation BIBREF19, BIBREF20, or monolingual repair systems, as in BIBREF21, which is concurrent work to ours  True post-editing datasets are typically small and expensive to create BIBREF22, hence synthetic training data has been created that uses original monolingual data as output for the sequence-to-sequence model, paired with an automatic back-translation BIBREF23 and/or round-trip translation as its input(s) BIBREF19, BIBREF21 
While previous work on automatic post-editing operated on the sentence level, the main novelty of this work is that our DocRepair model operates on groups of sentences and is thus able to fix consistency errors caused by the context-agnostic baseline MT system  We consider this strategy of sentence-level baseline translation and context-aware monolingual repair attractive when parallel document-level data is scarce 
For training, the DocRepair model only requires monolingual document-level data  While we create synthetic training data via round-trip translation similarly to earlier work BIBREF19, BIBREF21, note that we purposefully use sentence-level MT systems for this to create the types of consistency errors that we aim to fix with the context-aware DocRepair model  Not all types of consistency errors that we want to fix emerge from a round-trip translation, so access to parallel document-level data can be useful (Section SECREF34) 
Related Work ::: Document-level NMT
Neural models of MT that go beyond the sentence-level are an active research area BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF10, BIBREF9, BIBREF11  Typically, the main MT system is modified to take additional context as its input  One limitation of these approaches is that they assume that parallel document-level training data is available 
Closest to our work are two-pass models for document-level NMT BIBREF24, BIBREF11, where a second, context-aware model takes the translation and hidden representations of the sentence-level first-pass model as its input  The second-pass model can in principle be trained on a subset of the parallel training data BIBREF11, somewhat relaxing the assumption that all training data is at the document level 
Our work is different from this previous work in two main respects  Firstly, we show that consistency can be improved with only monolingual document-level training data  Secondly, the DocRepair model is decoupled from the first-pass MT system, which improves its portability 
Conclusions
We introduce the first approach to context-aware machine translation using only monolingual document-level data  We propose a monolingual DocRepair model to correct inconsistencies between sentence-level translations  The model performs automatic post-editing on a sequence of sentence-level translations, refining translations of sentences in context of each other  Our approach results in substantial improvements in translation quality as measured by BLEU, targeted contrastive evaluation of several discourse phenomena and human evaluation  Moreover, we perform error analysis and detect which discourse phenomena are hard to capture using only monolingual document-level data  While in the current work we used text fragments of 4 sentences, in future work we would like to consider longer contexts 
Acknowledgments
We would like to thank the anonymous reviewers for their comments  The authors also thank David Talbot and Yandex Machine Translation team for helpful discussions and inspiration  Ivan Titov acknowledges support of the European Research Council (ERC StG BroadSem 678254) and the Dutch National Science Foundation (NWO VIDI 639 022 518)  Rico Sennrich acknowledges support from the Swiss National Science Foundation (105212_169888), the European Union’s Horizon 2020 research and innovation programme (grant agreement no 825460), and the Royal Society (NAF\R1\180122) 
Context 3: Introduction
Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask  Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks  Low-resource languages still lag behind 
Endangered languages pose an additional challenge  The process of documenting an endangered language typically includes the creation of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored in large online linguistics archives  This process is often hindered by the Transcription Bottleneck: the linguistic fieldworker and the language community may not have time to transcribe all of the recordings and may only transcribe segments that are linguistically salient for publication or culturally significant for the creation of community resources 
With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina  We hope to ameliorate the resource gap and the transcription bottleneck in two ways  First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation)  In providing baselines and datasets splits, we hope to further facilitate research on low-resource NLP for this language through our data set  Research on low-resource speech recognition is particularly important in relieving the transcription bottleneck, while tackling the research challenges that speech synthesis and machine translation pose for such languages could lead to such systems being deployed to serve more under-represented communities 
The Mapudungun Language
Mapudungun (iso 639-3: arn) is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina BIBREF0  It is an isolate language and is classified as threatened by Ethnologue, hence the critical importance of all documentary efforts  Although the morphology of nouns is relatively simple, Mapudungun verb morphology is highly agglutinative and complex  Some analyses provide as many as 36 verb suffix slots BIBREF1  A typical complex verb form occurring in our corpus of spoken Mapudungun consists of five or six morphemes 
Mapudungun has several interesting grammatical properties  It is a polysynthetic language in the sense of BIBREF2; see BIBREF3 for explicit argumentation  As with other polysynthetic languages, Mapudungun has Noun Incorporation; however, it is unique insofar as the Noun appears to the right of the Verb, instead of to the left, as in most polysynthetic languages BIBREF4  One further distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms; that is, while subordinate clauses in Mapudungun closely resemble possessed nominals and may occur with an analytic marker resembling possessor agreement, there is no agreement inflection on the verb itself  One further remarkable property of Mapudungun is its inverse voice system of agreement, whereby the highest agreement is with the argument highest in an animacy hierarchy regardless of thematic role BIBREF5 
The Resource
The resource is comprised of 142 hours of spoken Mapudungun that was recorded during the AVENUE project BIBREF6 in 2001 to 2005  The data was recorded under a partnership between the AVENUE project, funded by the US National Science Foundation at Carnegie Mellon University, the Chilean Ministry of Education (Mineduc), and the Instituto de Estudios Indígenas at Universidad de La Frontera, originally spanning 170 hours of audio  We have recently cleaned the data and are releasing it publicly for the first time (although it has been shared with individual researchers in the past) along with NLP baselines 
The recordings were transcribed and translated into Spanish at the Instituto de Estudios Indígenas at Universidad de La Frontera  The corpus covers three dialects of Mapudungun: about 110 hours of Nguluche, 20 hours of Lafkenche and 10 hours of Pewenche  The three dialects are quite similar, with some minor semantic and phonetic differences  The fourth traditionally distinguished dialect, Huilliche, has several grammatical differences from the other three and is classified by Ethnologue as a separate language, iso 639-3: huh, and as nearly extinct 
The recordings are restricted to a single domain: primary, preventive, and treatment health care, including both Western and Mapuche traditional medicine  The recording sessions were conducted as interactive conversations so as to be natural in Mapuche culture, and they were open-ended, following an ethnographic approach  The interviewer was trained in these methods along with the use of the digital recording systems that were available at the time  We also followed human subject protocol  Each person signed a consent form to release the recordings for research purposes and the data have been accordingly anonymized  Because Machi (traditional Mapuche healers) were interviewed, we asked the transcribers to delete any culturally proprietary knowledge that a Machi may have revealed during the conversation  Similarly, we deleted any names or any information that may identify the participants 
The corpus is culturally relevant because it was created by Mapuche people, using traditional ways of relating to each other in conversations  They discussed personal experiences with primary health care in the traditional Mapuche system and the Chilean health care system, talking about illnesses and the way they were cured  The participants ranged from 16 years old to 100 years old, almost in equal numbers of men and women, and they were all native speakers of Mapudungun 
The Resource ::: Orthography
At the time of the collection and transcription of the corpus, the orthography of Mapudungun was not standardized  The Mapuche team at the Instituto de Estudios Indígenas (IEI – Institute for Indigenous Studies) developed a supra-dialectal alphabet that comprises 28 letters that cover 32 phones used in the three Mapudungun variants  The main criterion for choosing alphabetic characters was to use the current Spanish keyboard that was available on all computers in Chilean offices and schools  The alphabet used the same letters used in Spanish for those phonemes that sound like Spanish phonemes  Diacritics such as apostrophes were used for sounds that are not found in Spanish 
As a result, certain orthographic conventions that were made at the time deviate from the now-standard orthography of Mapudungun, Azumchefe  We plan to normalize the orthography of the corpus, and in fact a small sample has already been converted to the modern orthography

ground_truth: Byte-Pair Encoding perplexity  (BPE PPL),
BLEU-1,
BLEU-4,
ROUGE-L,
percentage of distinct unigram (D-1),
percentage of distinct bigrams(D-2),
user matching accuracy(UMA),
Mean Reciprocal Rank(MRR)
Pairwise preference over baseline(PP)

BLEU-1/4 and ROUGE-L, likelihood of generated recipes using identical input specifications but conditioned on ten different user profiles, user matching accuracy (UMA), Mean Reciprocal Rank (MRR), neural scoring model from BIBREF33 to measure recipe-level coherence

 Distinct-1/2, UMA = User Matching Accuracy, MRR
= Mean Reciprocal Rank, PP = Pairwise preference over baseline (evaluated for 310 recipe pairs per model)

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:  However, we believe that the original transcriptions will also be invaluable for academic, historical, and cultural purposes, hence we release the corpus using these conventions 
The Resource ::: Additional Annotations
In addition, the transcription includes annotations for noises and disfluencies including aborted words, mispronunciations, poor intelligibility, repeated and corrected words, false starts, hesitations, undefined sound or pronunciations, non-verbal articulations, and pauses  Foreign words, in this case Spanish words, are also labelled as such 
The Resource ::: Cleaning
The dialogues were originally recorded using a Sony DAT recorder (48kHz), model TCD-D8, and Sony digital stereo microphone, model ECM-DS70P  Transcription was performed with the TransEdit transcription tool v 1 1 beta 10, which synchronizes the transcribed text and the wave files 
However, we found that a non-trivial number of the utterance boundaries and speaker annotations were flawed  Also some recording sessions did not have a complete set of matching audio, transcription, and translation files  Hence, in an effort to provide a relatively “clean" corpus for modern computational experiments, we converted the encoding of the textual transcription from Latin-1 to Unicode, DOS to UNIX line endings, a now more standard text encoding format than what was used when the data was first collected  Additionally, we renamed a small portion of files which had been misnamed and removed several duplicate files 
Although all of the data was recorded with similar equipment in relatively quiet environments, the acoustics are not as uniform as we would like for building speech synthesizers  Thus we applied standardized power normalization  We also moved the boundaries of the turns to standardize the amount of leading and trailing silence in each turn  This is a standard procedure for speech recognition and synthesis datasets  Finally we used the techniques in BIBREF7 for found data to re-align the text to the audio and find out which turns are best (or worst) aligned so that we can select segments that give the most accurate alignments  Some of the misalignments may in part be due to varied orthography, and we intend, but have not yet, to investigate normalization of orthography (i e  spelling correction) to mitigate this 
The Resource ::: Training, Dev, and Test Splits
We create two training sets, one appropriate for single-speaker speech synthesis experiments, and one appropriate for multiple-speaker speech recognition and machine translation experiments  In both cases, our training, development, and test splits are performed at the dialogue level, so that all examples from each dialogue belong to exactly one of these sets 
For single-speaker speech synthesis, we only use the dialog turns of the speaker with the largest volume of data (nmlch – one of the interviewers)  The training set includes $221 8$ thousand sentences from 285 dialogues, with 12 and 46 conversations reserved for the development and test set 
For speech recognition experiments, we ensure that our test set includes unique speakers as well as speakers that overlap with the training set, in order to allow for comparisons of the ability of the speech recognition system to generalize over seen and new speakers  For consistency, we use the same dataset splits for the machine translation experiments  The statistics in Table reflect this split 
Applications
Our resource has the potential to be the basis of computational research in Mapudungun across several areas  Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis  The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation  We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction 
Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community  In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems  The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun  Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications 
The potential applications of our resource, however, are not exhausted in language technologies  The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture 
In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8  As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work 
Baseline Results
Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish 
Baseline Results ::: Speech Synthesis
In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription)  We used the same techniques here, as described above 
For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech  Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch  This gave us around 18250 segments

ground_truth:  MT system on the data released by BIBREF11

Transformer base, two-pass CADec model

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. How is non-standard pronunciation identified?
Context 3:  We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0)  This gave us 13 hours and 48 minutes of speech 
We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data  We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10  We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7 
The initial build gave an MCD on held out data of 6 483  While the 2000 best segment dataset gives an MCD of 5 551, which is a large improvement  The quality of the generated speech goes from understandable, only if you can see the text, to understandable, and transcribable even for non-Mapudungun speakers  We do not believe we are building the best synthesizer with our current (non-neural) techniques, but we do believe we are selecting the best training data for other statistical and neural training techniques in both speech synthesis and speech recognition 
Baseline Results ::: Speech Recognition
For speech recognition (ASR) we used Kaldi BIBREF11  As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings  In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme  In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques  The train/dev/test splits are across conversations, as described above 
Under the first setting, we obtained a 60% character error rate, while the generated lexicon significantly boosts performance, as our systems achieve a notably reduced 30% phone error rate  Naturally, these results are relatively far from the quality of ASR systems trained on large amounts of clean data such as those available in English  Given the quality of the recordings, and the lack of additional resources, we consider our results fairly reasonable and they would still be usable for simple dialog-like tasks  We anticipate, though, that one could significantly improve ASR quality over our dataset, by using in-domain language models, or by training end-to-end neural recognizers leveraging languages with similar phonetic inventories BIBREF12 or by using the available Spanish translations in a multi-source scenario BIBREF13 
Baseline Results ::: Mapudungun–Spanish Machine Translation
We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15  We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages  We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads  We use dropout BIBREF17 with $0 4$ probability as well as label smoothing set to $0 1$  We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs 
The baseline results using different portions of the training set (10k, 50k, 100k, and all (220k) parallel sentences) on both translation directions are presented in Table , using detokenized BLEU BIBREF19 (a standard MT metric) and chrF BIBREF20 (a metric that we consider to be more appropriate for polysynthetic languages, as it does not rely on word n-grams) computed with the sacreBLEU toolkit BIBREF21  It it worth noting the difference in quality between the two directions, with translation into Spanish reaching 20 4 (almost 21) BLEU points in the development set, while the opposite direction (translating into Mapudungun) shows about a 7 BLEU points worse performance  This is most likely due to Mapudungun being a polysynthetic language, with its complicated morphology posing a challenge for proper generation 
Related Work
Mapudungun grammar has been studied since the arrival of European missionaries and colonizers hundreds of years ago  More recent descriptions of Mapudungun grammar BIBREF1 and BIBREF0 informed the collection of the resource that we are presenting in this paper 
Portions of our resource have been used in early efforts to build language systems for Mapudungun  In particular, BIBREF22 focused on Mapudungun morphology in order to create spelling correction systems, while BIBREF23, BIBREF6, BIBREF24, and BIBREF25 developed hybrid rule- and phrase-based Statistical Machine Translation systems 
Naturally, similar works in collecting corpora in Indigenous languages of Latin America are abundant, but very few, if any, have the scale and potential of our resource to be useful in many downstream language-specific and inter-disciplinary applications  A general overview of the state of NLP for the under-represented languages of the Americas can be found at BIBREF26  To name a few of the many notable works, BIBREF27 created a parallel Mixtec-Spanish corpus for Machine Translation and BIBREF28 created lexical resources for Arapaho, while BIBREF29 and BIBREF30 focused on building speech corpora for Southern Quechua and Chatino respectively 
Conclusion
With this work we present a resource that will be extremely useful for building language systems in an endangered, under-represented language, Mapudungun  We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results  The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource 
Our resource could also be used for ethnographic and anthropological research into the Mapuche culture, and has the potential to contribute to intercultural bilingual education, preservation activities and further general advancement of the Mapudungun-speaking community 
Acknowledgements
The data collection described in this paper was supported by NSF grants IIS-0121631 (AVENUE) and IIS-0534217 (LETRAS), with supplemental funding from NSF's Office of International Science and Education  Preliminary funding for work on Mapudungun was also provided by DARPA The experimental material is based upon work generously supported by the National Science Foundation under grant 1761548 

ground_truth: Unanswerable

Original transcription was labeled with additional labels in [] brackets with nonstandard pronunciation.