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 were the baselines?
Context 1: Introduction
Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words  Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3   Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system 
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6   We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers  Liptchinsky et al  BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout 
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks  Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10   We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR  As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs 
It is possible to increase the capacity of the Jasper model by stacking these operations  Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters)  We use residual connections to enable this level of depth  We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR) 
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2 95% WER and SOTA results among end-to-end models on LibriSpeech test-other  We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S)  Using only greedy decoding without a language model we achieve 3 86% WER on LibriSpeech test-clean 
This paper makes the following contributions:
Jasper Architecture
Jasper is a family of end-to-end ASR models that replace acoustic and pronunciation models with a convolutional neural network  Jasper uses mel-filterbank features calculated from 20ms windows with a 10ms overlap, and outputs a probability distribution over characters per frame  Jasper has a block architecture: a Jasper INLINEFORM0 x INLINEFORM1 model has INLINEFORM2 blocks, each with INLINEFORM3 sub-blocks  Each sub-block applies the following operations: a 1D-convolution, batch norm, ReLU, and dropout  All sub-blocks in a block have the same number of output channels 
Each block input is connected directly into the last sub-block via a residual connection  The residual connection is first projected through a 1x1 convolution to account for different numbers of input and output channels, then through a batch norm layer  The output of this batch norm layer is added to the output of the batch norm layer in the last sub-block  The result of this sum is passed through the activation function and dropout to produce the output of the sub-block 
The sub-block architecture of Jasper was designed to facilitate fast GPU inference  Each sub-block can be fused into a single GPU kernel: dropout is not used at inference-time and is eliminated, batch norm can be fused with the preceding convolution, ReLU clamps the result, and residual summation can be treated as a modified bias term in this fused operation 
All Jasper models have four additional convolutional blocks: one pre-processing and three post-processing  See Figure FIGREF7 and Table TABREF8 for details 
We also build a variant of Jasper, Jasper Dense Residual (DR)  Jasper DR follows DenseNet BIBREF15 and DenseRNet BIBREF16 , but instead of having dense connections within a block, the output of a convolution block is added to the inputs of all the following blocks  While DenseNet and DenseRNet concatenates the outputs of different layers, Jasper DR adds them in the same way that residuals are added in ResNet  As explained below, we find addition to be as effective as concatenation 
Normalization and Activation
In our study, we evaluate performance of models with:
3 types of normalization: batch norm BIBREF10 , weight norm BIBREF9 , and layer norm BIBREF17
3 types of rectified linear units: ReLU, clipped ReLU (cReLU), and leaky ReLU (lReLU)
2 types of gated units: gated linear units (GLU) BIBREF8 , and gated activation units (GAU) BIBREF18
All experiment results are shown in Table TABREF15   We first experimented with a smaller Jasper5x3 model to pick the top 3 settings before training on larger Jasper models  We found that layer norm with GAU performed the best on the smaller model  Layer norm with ReLU and batch norm with ReLU came second and third in our tests  Using these 3, we conducted further experiments on a larger Jasper10x4  For larger models, we noticed that batch norm with ReLU outperformed other choices  Thus, leading us to decide on batch normalization and ReLU for our architecture 
During batching, all sequences are padded to match the longest sequence  These padded values caused issues when using layer norm  We applied a sequence mask to exclude padding values from the mean and variance calculation  Further, we computed mean and variance over both the time dimension and channels similar to the sequence-wise normalization proposed by Laurent et al  BIBREF19   In addition to masking layer norm, we additionally applied masking prior to the convolution operation, and masking the mean and variance calculation in batch norm  These results are shown in Table TABREF16   Interestingly, we found that while masking before convolution gives a lower WER, using masks for both convolutions and batch norm results in worse performance 
As a final note, we found that training with weight norm was very unstable leading to exploding activations 
Residual Connections
For models deeper than Jasper 5x3, we observe consistently that residual connections are necessary for training to converge  In addition to the simple residual and dense residual model described above, we investigated DenseNet BIBREF15 and DenseRNet BIBREF16 variants of Jasper  Both connect the outputs of each sub-block to the inputs of following sub-blocks within a block  DenseRNet, similar to Dense Residual, connects the output of each output of each block to the input of all following blocks  DenseNet and DenseRNet combine residual connections using concatenation whereas Residual and Dense Residual use addition  We found that Dense Residual and DenseRNet perform similarly with each performing better on specific subsets of LibriSpeech  We decided to use Dense Residual for subsequent experiments  The main reason is that due to concatenation, the growth factor for DenseNet and DenseRNet requires tuning for deeper models whereas Dense Residual simply just repeats a sub-blocks 
Language Model
A language model (LM) is a probability distribution over arbitrary symbol sequences INLINEFORM0 such that more likely sequences are assigned high probabilities  LMs are frequently used to condition beam search  During decoding, candidates are evaluated using both acoustic scores and LM scores  Traditional N-gram LMs have been augmented with neural LMs in recent work BIBREF20 , BIBREF21 , BIBREF22  
We experiment with statistical N-gram language models BIBREF23 and neural Transformer-XL BIBREF11 models  Our best results use acoustic and word-level N-gram language models to generate a candidate list using beam search with a width of 2048  Next, an external Transformer-XL LM rescores the final list  All LMs were trained on datasets independently from acoustic models  We show results with the neural LM in our Results section  We observed a strong correlation between the quality of the neural LM (measured by perplexity) and WER as shown in Figure FIGREF20  
NovoGrad
For training, we use either Stochastic Gradient Descent (SGD) with momentum or our own NovoGrad, an optimizer similar to Adam BIBREF14 , except that its second moments are computed per layer instead of per weight  Compared to Adam, it reduces memory consumption and we find it to be more numerically stable 
At each step INLINEFORM0 , NovoGrad computes the stochastic gradient INLINEFORM1 following the regular forward-backward pass  Then the second-order moment INLINEFORM2 is computed for each layer INLINEFORM3 similar to ND-Adam BIBREF27 : DISPLAYFORM0
The second-order moment INLINEFORM0 is used to re-scale gradients INLINEFORM1 before calculating the first-order moment INLINEFORM2 : DISPLAYFORM0
If L2-regularization is used, a weight decay INLINEFORM0 is added to the re-scaled gradient (as in AdamW BIBREF28 ): DISPLAYFORM0
Finally, new weights are computed using the learning rate INLINEFORM0 : DISPLAYFORM0
Using NovoGrad instead of SGD with momentum, we decreased the WER on dev-clean LibriSpeech from 4 00% to 3 64%, a relative improvement of 9% for Jasper DR 10x5  We will further analyze NovoGrad in forthcoming work 
Results
We evaluate Jasper across a number of datasets in various domains  In all experiments, we use dropout and weight decay as regularization  At training time, we use speed perturbation with fixed +/-10% BIBREF29 for LibriSpeech  For WSJ and Hub5'00, we use a random speed perturbation factor between [-10%, 10%] as each utterance is fed into the model  All models have been trained on NVIDIA DGX-1 in mixed precision BIBREF30 using OpenSeq2Seq BIBREF31   Source code, training configurations, and pretrained models are available 
Read Speech
We evaluated the performance of Jasper on two read speech datasets: LibriSpeech and Wall Street Journal (WSJ)  For LibriSpeech, we trained Jasper DR 10x5 using our NovoGrad optimizer for 400 epochs  We achieve SOTA performance on the test-clean subset and SOTA among end-to-end speech recognition models on test-other 
We trained a smaller Jasper 10x3 model with SGD with momentum optimizer for 400 epochs on a combined WSJ dataset (80 hours): LDC93S6A (WSJ0) and LDC94S13A (WSJ1)  The results are provided in Table TABREF29  
Conversational Speech
We also evaluate the Jasper model's performance on a conversational English corpus  The Hub5 Year 2000 (Hub5'00) evaluation (LDC2002S09, LDC2005S13) is widely used in academia  It is divided into two subsets: Switchboard (SWB) and Callhome (CHM)  The training data for both the acoustic and language models consisted of the 2000hr Fisher+Switchboard training data (LDC2004S13, LDC2005S13, LDC97S62)  Jasper DR 10x5 was trained using SGD with momentum for 50 epochs  We compare to other models trained using the same data and report Hub5'00 results in Table TABREF31  
We obtain good results for SWB  However, there is work to be done to improve WER on harder tasks such as CHM 
Conclusions
We have presented a new family of neural architectures for end-to-end speech recognition  Inspired by wav2letter's convolutional approach, we build a deep and scalable model, which requires a well-designed residual topology, effective regularization, and a strong optimizer  As our architecture studies demonstrated, a combination of standard components leads to SOTA results on LibriSpeech and competitive results on other benchmarks  Our Jasper architecture is highly efficient for training and inference, and serves as a good baseline approach on top of which to explore more sophisticated regularization, data augmentation, loss functions, language models, and optimization strategies  We are interested to see if our approach can continue to scale to deeper models and larger datasets 
Context 2: 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  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)

ground_truth: Unanswerable

LF-MMI Attention
Seq2Seq 
RNN-T 
Char E2E LF-MMI 
Phone E2E LF-MMI 
CTC + Gram-CTC

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 2:  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  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 
Context 3: Introduction
In practice, it is often difficult and costly to annotate sufficient training data for diverse application domains on-the-fly  We may have sufficient labeled data in an existing domain (called the source domain), but very few or no labeled data in a new domain (called the target domain)  This issue has motivated research on cross-domain sentiment classification, where knowledge in the source domain is transferred to the target domain in order to alleviate the required labeling effort 
One key challenge of domain adaptation is that data in the source and target domains are drawn from different distributions  Thus, adaptation performance will decline with an increase in distribution difference  Specifically, in sentiment analysis, reviews of different products have different vocabulary  For instance, restaurants reviews would contain opinion words such as “tender”, “tasty”, or “undercooked” and movie reviews would contain “thrilling”, “horrific”, or “hilarious”  The intersection between these two sets of opinion words could be small which makes domain adaptation difficult 
Several techniques have been proposed for addressing the problem of domain shifting  The aim is to bridge the source and target domains by learning domain-invariant feature representations so that a classifier trained on a source domain can be adapted to another target domain  In cross-domain sentiment classification, many works BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 utilize a key intuition that domain-specific features could be aligned with the help of domain-invariant features (pivot features)  For instance, “hilarious” and “tasty” could be aligned as both of them are relevant to “good” 
Despite their promising results, these works share two major limitations  First, they highly depend on the heuristic selection of pivot features, which may be sensitive to different applications  Thus the learned new representations may not effectively reduce the domain difference  Furthermore, these works only utilize the unlabeled target data for representation learning while the sentiment classifier was solely trained on the source domain  There have not been many studies on exploiting unlabeled target data for refining the classifier, even though it may contain beneficial information  How to effectively leverage unlabeled target data still remains an important challenge for domain adaptation 
In this work, we argue that the information from unlabeled target data is beneficial for domain adaptation and we propose a novel Domain Adaptive Semi-supervised learning framework (DAS) to better exploit it  Our main intuition is to treat the problem as a semi-supervised learning task by considering target instances as unlabeled data, assuming the domain distance can be effectively reduced through domain-invariant representation learning  Specifically, the proposed approach jointly performs feature adaptation and semi-supervised learning in a multi-task learning setting  For feature adaptation, it explicitly minimizes the distance between the encoded representations of the two domains  On this basis, two semi-supervised regularizations – entropy minimization and self-ensemble bootstrapping – are jointly employed to exploit unlabeled target data for classifier refinement 
We evaluate our method rigorously under multiple experimental settings by taking label distribution and corpus size into consideration  The results show that our model is able to obtain significant improvements over strong baselines  We also demonstrate through a series of analysis that the proposed method benefits greatly from incorporating unlabeled target data via semi-supervised learning, which is consistent with our motivation  Our datasets and source code can be obtained from https://github com/ruidan/DAS 
Related Work
Domain Adaptation: The majority of feature adaptation methods for sentiment analysis rely on a key intuition that even though certain opinion words are completely distinct for each domain, they can be aligned if they have high correlation with some domain-invariant opinion words (pivot words) such as “excellent” or “terrible”  Blitzer et al  ( BIBREF0 ) proposed a method based on structural correspondence learning (SCL), which uses pivot feature prediction to induce a projected feature space that works well for both the source and the target domains  The pivot words are selected in a way to cover common domain-invariant opinion words  Subsequent research aims to better align the domain-specific words BIBREF1 , BIBREF5 , BIBREF3 such that the domain discrepancy could be reduced  More recently, Yu and Jiang ( BIBREF4 ) borrow the idea of pivot feature prediction from SCL and extend it to a neural network-based solution with auxiliary tasks  In their experiment, substantial improvement over SCL has been observed due to the use of real-valued word embeddings  Unsupervised representation learning with deep neural networks (DNN) such as denoising autoencoders has also been explored for feature adaptation BIBREF6 , BIBREF7 , BIBREF8   It has been shown that DNNs could learn transferable representations that disentangle the underlying factors of variation behind data samples 
Although the aforementioned methods aim to reduce the domain discrepancy, they do not explicitly minimize the distance between distributions, and some of them highly rely on the selection of pivot features  In our method, we formally construct an objective for this purpose  Similar ideas have been explored in many computer vision problems, where the representations of the underlying domains are encouraged to be similar through explicit objectives BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 such as maximum mean discrepancy (MMD) BIBREF14   In NLP tasks, Li et al  ( BIBREF15 ) and Chen et al  ( BIBREF16 ) both proposed using adversarial training framework for reducing domain difference  In their model, a sub-network is added as a domain discriminator while deep features are learned to confuse the discriminator  The feature adaptation component in our model shares similar intuition with MMD and adversary training  We will show a detailed comparison with them in our experiments 
Semi-supervised Learning: We attempt to treat domain adaptation as a semi-supervised learning task by considering the target instances as unlabeled data  Some efforts have been initiated on transfer learning from unlabeled data BIBREF17 , BIBREF18 , BIBREF19   In our model, we reduce the domain discrepancy by feature adaptation, and thereafter adopt semi-supervised learning techniques to learn from unlabeled data  Primarily motivated by BIBREF20 and BIBREF21 , we employed entropy minimization and self-ensemble bootstrapping as regularizations to incorporate unlabeled data  Our experimental results show that both methods are effective when jointly trained with the feature adaptation objective, which confirms to our motivation 
Notations and Model Overview
We conduct most of our experiments under an unsupervised domain adaptation setting, where we have no labeled data from the target domain  Consider two sets INLINEFORM0 and INLINEFORM1   INLINEFORM2 is from the source domain with INLINEFORM3 labeled examples, where INLINEFORM4 is a one-hot vector representation of sentiment label and INLINEFORM5 denotes the number of classes  INLINEFORM6 is from the target domain with INLINEFORM7 unlabeled examples  INLINEFORM8 denotes the total number of training documents including both labeled and unlabeled  We aim to learn a sentiment classifier from INLINEFORM13 and INLINEFORM14 such that the classifier would work well on the target domain  We also present some results under a setting where we assume that a small number of labeled target examples are available (see Figure FIGREF27 ) 
For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier  We aim to learn feature representations that are domain-invariant and at the same time discriminative on both domains, thus we simultaneously consider three factors in our objective: (1) minimize the classification error on the labeled source examples; (2) minimize the domain discrepancy; and (3) leverage unlabeled data via semi-supervised learning 
Suppose we already have the encoded features of documents INLINEFORM0 (see Section SECREF10 ), the objective function for purpose (1) is thus the cross entropy loss on the labeled source examples DISPLAYFORM0
where INLINEFORM0 denotes the predicted label distribution  In the following subsections, we will explain how to perform feature adaptation and domain adaptive semi-supervised learning in details for purpose (2) and (3) respectively 
Feature Adaptation
Unlike prior works BIBREF0 , BIBREF4 , our method does not attempt to align domain-specific words through pivot words  In our preliminary experiments, we found that word embeddings pre-trained on a large corpus are able to adequately capture this information  As we will later show in our experiments, even without adaptation, a naive neural network classifier with pre-trained word embeddings can already achieve reasonably good results 
We attempt to explicitly minimize the distance between the source and target feature representations ( INLINEFORM0 and INLINEFORM1 )  A few methods from literature can be applied such as Maximum Mean Discrepancy (MMD) BIBREF14 or adversary training BIBREF15 , BIBREF16   The main idea of MMD is to estimate the distance between two distributions as the distance between sample means of the projected embeddings in Hilbert space  MMD is implicitly computed through a characteristic kernel, which is used to ensure that the sample mean is injective, leading to the MMD being zero if and only if the distributions are identical  In our implementation, we skip the mapping procedure induced by a characteristic kernel for simplifying the computation and learning  We simply estimate the distribution distance as the distance between the sample means in the current embedding space  Although this approximation cannot preserve all statistical features of the underlying distributions, we find it performs comparably to MMD on our problem  The following equations formally describe the feature adaptation loss INLINEFORM2 : DISPLAYFORM0
INLINEFORM0 normalization is applied on the mean representations INLINEFORM1 and INLINEFORM2 , rescaling the vectors such that all entries sum to 1  We adopt a symmetric version of KL divergence BIBREF12 as the distance function  Given two distribution vectors INLINEFORM3 , INLINEFORM4  
Domain Adaptive Semi-supervised Learning (DAS)
We attempt to exploit the information in target data through semi-supervised learning objectives, which are jointly trained with INLINEFORM0 and INLINEFORM1   Normally, to incorporate target data, we can minimize the cross entropy loss between the true label distributions INLINEFORM2 and the predicted label distributions INLINEFORM3 over target samples  The challenge here is that INLINEFORM4 is unknown, and thus we attempt to estimate it via semi-supervised learning  We use entropy minimization and bootstrapping for this purpose  We will later show in our experiments that both methods are effective, and jointly employing them overall yields the best results 
Entropy Minimization: In this method, INLINEFORM0 is estimated as the predicted label distribution INLINEFORM1 , which is a function of INLINEFORM2 and INLINEFORM3   The loss can thus be written as DISPLAYFORM0
Assume the domain discrepancy can be effectively reduced through feature adaptation, by minimizing the entropy penalty, training of the classifier is influenced by the unlabeled target data and will generally maximize the margins between the target examples and the decision boundaries, increasing the prediction confidence on the target domain 
Self-ensemble Bootstrapping: Another way to estimate INLINEFORM0 corresponds to bootstrapping  The idea is to estimate the unknown labels as the predictions of the model learned from the previous round of training  Bootstrapping has been explored for domain adaptation in previous works BIBREF18 , BIBREF19   However, in their methods, domain discrepancy was not explicitly minimized via feature adaptation  Applying bootstrapping or other semi-supervised learning techniques in this case may worsen the results as the classifier can perform quite bad on the target data 
[t] Pseudocode for training DAS INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 INLINEFORM4 = ensembling momentum, INLINEFORM5 INLINEFORM6 = weight ramp-up function INLINEFORM7 INLINEFORM8 INLINEFORM9 each minibatch INLINEFORM10 , INLINEFORM11 , INLINEFORM12 in
INLINEFORM0 , INLINEFORM1 , INLINEFORM2 compute loss INLINEFORM3 on INLINEFORM4 compute loss INLINEFORM5 on INLINEFORM6 compute loss INLINEFORM7 on INLINEFORM8 compute loss INLINEFORM9 on INLINEFORM10 INLINEFORM11
update network parameters INLINEFORM0 , for INLINEFORM1 INLINEFORM2 INLINEFORM3
Inspired by the ensembling method proposed in BIBREF21 , we estimate INLINEFORM0 by forming ensemble predictions of labels during training, using the outputs on different training epochs  The loss is formulated as follows: DISPLAYFORM0
where INLINEFORM0 denotes the estimated labels computed on the ensemble predictions from different epochs  The loss is applied on all documents  It serves for bootstrapping on the unlabeled target data, and it also serves as a regularization that encourages the network predictions to be consistent in different training epochs  INLINEFORM1 is jointly trained with INLINEFORM2 , INLINEFORM3 , and INLINEFORM4   Algorithm SECREF6 illustrates the overall training process of the proposed domain adaptive semi-supervised learning (DAS) framework 
In Algorithm SECREF6 , INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are weights to balance the effects of INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 respectively  INLINEFORM6 and INLINEFORM7 are constant hyper-parameters  We set INLINEFORM8 as a Gaussian curve to ramp up the weight from 0 to INLINEFORM9   This is to ensure the ramp-up of the bootstrapping loss component is slow enough in the beginning of the training  After each training epoch, we compute INLINEFORM10 which denotes the predictions made by the network in current epoch, and then the ensemble prediction INLINEFORM11 is updated as a weighted average of the outputs from previous epochs and the current epoch, with recent epochs having larger weight  For generating estimated labels INLINEFORM12 , INLINEFORM13 is converted to a one-hot vector where the entry with the maximum value is set to one and other entries are set to zeros  The self-ensemble bootstrapping is a generalized version of bootstrappings that only use the outputs from the previous round of training BIBREF18 , BIBREF19   The ensemble prediction is likely to be closer to the correct, unknown labels of the target data 
CNN Encoder Implementation
We have left the feature encoder INLINEFORM0 unspecified, for which, a few options can be considered  In our implementation, we adopt a one-layer CNN structure from previous works BIBREF22 , BIBREF4 , as it has been demonstrated to work well for sentiment classification tasks  Given a review document INLINEFORM1 consisting of INLINEFORM2 words, we begin by associating each word with a continuous word embedding BIBREF23 INLINEFORM3 from an embedding matrix INLINEFORM4 , where INLINEFORM5 is the vocabulary size and INLINEFORM6 is the embedding dimension  INLINEFORM7 is jointly updated with other network parameters during training  Given a window of dense word embeddings INLINEFORM8 , the convolution layer first concatenates these vectors to form a vector INLINEFORM9 of length INLINEFORM10 and then the output vector is computed by Equation ( EQREF11 ): DISPLAYFORM0
INLINEFORM0 , INLINEFORM1 is the parameter set of the encoder INLINEFORM2 and is shared across all windows of the sequence  INLINEFORM3 is an element-wise non-linear activation function  The convolution operation can capture local contextual dependencies of the input sequence and the extracted feature vectors are similar to INLINEFORM4 -grams  After the convolution operation is applied to the whole sequence, we obtain a list of hidden vectors INLINEFORM5   A max-over-time pooling layer is applied to obtain the final vector representation INLINEFORM6 of the input document 
Datasets and Experimental Settings
Existing benchmark datasets such as the Amazon benchmark BIBREF0 typically remove reviews with neutral labels in both domains  This is problematic as the label information of the target domain is not accessible in an unsupervised domain adaptation setting  Furthermore, removing neutral instances may bias the dataset favorably for max-margin-based algorithms like ours, since the resulting dataset has all uncertain labels removed, leaving only high confidence examples  Therefore, we construct new datasets by ourselves  The results on the original Amazon benchmark is qualitatively similar, and we present them in Appendix SECREF6 for completeness since most of previous works reported results on it 
Small-scale datasets: Our new dataset was derived from the large-scale Amazon datasets released by McAuley et al  ( BIBREF24 )  It contains four domains: Book (BK), Electronics (E), Beauty (BT), and Music (M)  Each domain contains two datasets  Set 1 contains 6000 instances with exactly balanced class labels, and set 2 contains 6000 instances that are randomly sampled from the large dataset, preserving the original label distribution, which we believe better reflects the label distribution in real life  The examples in these two sets do not overlap  Detailed statistics of the generated datasets are given in Table TABREF9  
In all our experiments on the small-scale datasets, we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain  Since we cannot control the label distribution of unlabeled data during training, we consider two different settings:
Setting (1): Only set 1 of the target domain is used as the unlabeled set  This tells us how the method performs in a condition when the target domain has a close-to-balanced label distribution  As we also evaluate on set 1 of the target domain, this is also considered as a transductive setting 
Setting (2): Set 2 from both the source and target domains are used as unlabeled sets

ground_truth: Unanswerable

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

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 are the source and target domains?
Context 3:  Since set 2 is directly sampled from millions of reviews, it better reflects real-life sentiment distribution 
Large-scale datasets: We further conduct experiments on four much larger datasets: IMDB (I), Yelp2014 (Y), Cell Phone (C), and Baby (B)  IMDB and Yelp2014 were previously used in BIBREF25 , BIBREF26   Cell phone and Baby are from the large-scale Amazon dataset BIBREF24 , BIBREF27   Detailed statistics are summarized in Table TABREF9   We keep all reviews in the original datasets and consider a transductive setting where all target examples are used for both training (without label information) and evaluation  We perform sampling to balance the classes of labeled source data in each minibatch INLINEFORM3 during training 
Selection of Development Set
Ideally, the development set should be drawn from the same distribution as the test set  However, under the unsupervised domain adaptation setting, we do not have any labeled target data at training phase which could be used as development set  In all of our experiments, for each pair of domains, we instead sample 1000 examples from the training set of the source domain as development set  We train the network for a fixed number of epochs, and the model with the minimum classification error on this development set is saved for evaluation  This approach works well on most of the problems since the target domain is supposed to behave like the source domain if the domain difference is effectively reduced 
Another problem is how to select the values for hyper-parameters  If we tune INLINEFORM0 and INLINEFORM1 directly on the development set from the source domain, most likely both of them will be set to 0, as unlabeled target data is not helpful for improving in-domain accuracy of the source domain  Other neural network models also have the same problem for hyper-parameter tuning  Therefore, our strategy is to use the development set from the target domain to optimize INLINEFORM2 and INLINEFORM3 for one problem (e g , we only do this on E INLINEFORM4 BK), and fix their values on the other problems  This setting assumes that we have at least two labeled domains such that we can optimize the hyper-parameters, and then we fix them for other new unlabeled domains to transfer to 
Training Details and Hyper-parameters
We initialize word embeddings using the 300-dimension GloVe vectors supplied by Pennington et al , ( BIBREF28 ), which were trained on 840 billion tokens from the Common Crawl  For each pair of domains, the vocabulary consists of the top 10000 most frequent words  For words in the vocabulary but not present in the pre-trained embeddings, we randomly initialize them 
We set hyper-parameters of the CNN encoder following previous works BIBREF22 , BIBREF4 without specific tuning on our datasets  The window size is set to 3 and the size of the hidden layer is set to 300  The nonlinear activation function is Relu  For regularization, we also follow their settings and employ dropout with probability set to 0 5 on INLINEFORM0 before feeding it to the output layer INLINEFORM1 , and constrain the INLINEFORM2 -norm of the weight vector INLINEFORM3 , setting its max norm to 3 
On the small-scale datasets and the Aamzon benchmark, INLINEFORM0 and INLINEFORM1 are set to 200 and 1, respectively, tuned on the development set of task E INLINEFORM2 BK under setting 1  On the large-scale datasets, INLINEFORM3 and INLINEFORM4 are set to 500 and 0 2, respectively, tuned on I INLINEFORM5 Y  We use a Gaussian curve INLINEFORM6 to ramp up the weight of the bootstrapping loss INLINEFORM7 from 0 to INLINEFORM8 , where INLINEFORM9 denotes the maximum number of training epochs  We train 30 epochs for all experiments  We set INLINEFORM10 to 3 and INLINEFORM11 to 0 5 for all experiments 
The batch size is set to 50 on the small-scale datasets and the Amazon benchmark  We increase the batch size to 250 on the large-scale datasets to reduce the number of iterations  RMSProp optimizer with learning rate set to 0 0005 is used for all experiments 
Models for Comparison
We compare with the following baselines:
(1) Naive: A non-domain-adaptive baseline with bag-of-words representations and SVM classifier trained on the source domain 
(2) mSDA BIBREF7 : This is the state-of-the-art method based on discrete input features  Top 1000 bag-of-words features are kept as pivot features  We set the number of stacked layers to 3 and the corruption probability to 0 5 
(3) NaiveNN: This is a non-domain-adaptive CNN trained on source domain, which is a variant of our model by setting INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 to zeros 
(4) AuxNN BIBREF4 : This is a neural model that exploits auxiliary tasks, which has achieved state-of-the-art results on cross-domain sentiment classification  The sentence encoder used in this model is the same as ours 
(5) ADAN BIBREF16 : This method exploits adversarial training to reduce representation difference between domains  The original paper uses a simple feedforward network as encoder  For fair comparison, we replace it with our CNN-based encoder  We train 5 iterations on the discriminator per iteration on the encoder and sentiment classifier as suggested in their paper 
(6) MMD: MMD has been widely used for minimizing domain discrepancy on images  In those works BIBREF9 , BIBREF13 , variants of deep CNNs are used for encoding images and the MMDs of multiple layers are jointly minimized  In NLP, adding more layers of CNNs may not be very helpful and thus those models from image-related tasks can not be directly applied to our problem  To compare with MMD-based method, we train a model that jointly minimize the classification loss INLINEFORM0 on the source domain and MMD between INLINEFORM1 and INLINEFORM2   For computing MMD, we use a Gaussian RBF which is a common choice for characteristic kernel 
In addition to the above baselines, we also show results of different variants of our model  DAS as shown in Algorithm SECREF6 denotes our full model  DAS-EM denotes the model with only entropy minimization for semi-supervised learning (set INLINEFORM0 )  DAS-SE denotes the model with only self-ensemble bootstrapping for semi-supervised learning (set INLINEFORM1 )  FANN (feature-adaptation neural network) denotes the model without semi-supervised learning performed (set both INLINEFORM2 and INLINEFORM3 to zeros) 
Main Results
Figure FIGREF17 shows the comparison of adaptation results (see Appendix SECREF7 for the exact numerical numbers)  We report classification accuracy on the small-scale dataset  For the large-scale dataset, macro-F1 is instead used since the label distribution in the test set is extremely unbalanced  Key observations are summarized as follows  (1) Both DAS-EM and DAS-SE perform better in most cases compared with ADAN, MDD, and FANN, in which only feature adaptation is performed  This demonstrates the effectiveness of the proposed domain adaptive semi-supervised learning framework  DAS-EM is more effective than DAS-SE in most cases, and the full model DAS with both techniques jointly employed overall has the best performance  (2) When comparing the two settings on the small-scale dataset, all domain-adaptive methods generally perform better under setting 1  In setting 1, the target examples are balanced in classes, which can provide more diverse opinion-related features  However, when considering unsupervised domain adaptation, we should not presume the label distribution of the unlabeled data  Thus, it is necessary to conduct experiments using datasets that reflect real-life sentiment distribution as what we did on setting2 and the large-scale dataset  Unfortunately, this is ignored by most of previous works  (3) Word-embeddings are very helpful, as we can see even NaiveNN can substantially outperform mSDA on most tasks 
To see the effect of semi-supervised learning alone, we also conduct experiments by setting INLINEFORM0 to eliminate the effect of feature adaptation  Both entropy minimization and bootstrapping perform very badly in this setting  Entropy minimization gives almost random predictions with accuracy below 0 4, and the results of bootstrapping are also much lower compared to NaiveNN  This suggests that the feature adaptation component is essential  Without it, the learned target representations are less meaningful and discriminative  Applying semi-supervised learning in this case is likely to worsen the results 
Further Analysis
In Figure FIGREF23 , we show the change of accuracy with respect to the percentage of unlabeled data used for training on three particular problems under setting 1  The value at INLINEFORM0 denotes the accuracies of NaiveNN which does not utilize any target data  For DAS, we observe a nonlinear increasing trend where the accuracy quickly improves at the beginning, and then gradually stabilizes  For other methods, this trend is less obvious, and adding more unlabeled data sometimes even worsen the results  This finding again suggests that the proposed approach can better exploit the information from unlabeled data 
We also conduct experiments under a setting with a small number of labeled target examples available  Figure FIGREF27 shows the change of accuracy with respect to the number of labeled target examples added for training  We can observe that DAS is still more effective under this setting, while the performance differences to other methods gradually decrease with the increasing number of labeled target examples 
CNN Filter Analysis
In this subsection, we aim to better understand DAS by analyzing sentiment-related CNN filters  To do that, 1) we first select a list of the most related CNN filters for predicting each sentiment label (positive, negative neutral)  Those filters can be identified according to the learned weights INLINEFORM0 of the output layer INLINEFORM1   Higher weight indicates stronger relatedness  2) Recall that in our implementation, each CNN filter has a window size of 3 with Relu activation  We can thus represent each selected filter as a ranked list of trigrams with highest activation values 
We analyze the CNN filters learned by NaiveNN, FANN and DAS respectively on task E INLINEFORM0 BT under setting 1  We focus on E INLINEFORM1 BT for study because electronics and beauty are very different domains and each of them has a diverse set of domain-specific sentiment expressions  For each method, we identify the top 10 most related filters for each sentiment label, and extract the top trigrams of each selected filter on both source and target domains  Since labeled source examples are used for training, we find the filters learned by the three methods capture similar expressions on the source domain, containing both domain-invariant and domain-specific trigrams  On the target domain, DAS captures more target-specific expressions compared to the other two methods  Due to space limitation, we only present a small subset of positive-sentiment-related filters in Table TABREF34   The complete results are provided in Appendix SECREF8   From Table TABREF34 , we can observe that the filters learned by NaiveNN are almost unable to capture target-specific sentiment expressions, while FANN is able to capture limited target-specific words such as “clean” and “scent”  The filters learned by DAS are more domain-adaptive, capturing diverse sentiment expressions in the target domain 
Conclusion
In this work, we propose DAS, a novel framework that jointly performs feature adaptation and semi-supervised learning  We have demonstrated through multiple experiments that DAS can better leverage unlabeled data, and achieve substantial improvements over baseline methods  We have also shown that feature adaptation is an essential component, without which, semi-supervised learning is not able to function properly  The proposed framework could be potentially adapted to other domain adaptation tasks, which is the focus of our future studies 
Results on Amazon Benchmark
Most previous works BIBREF0 , BIBREF1 , BIBREF6 , BIBREF7 , BIBREF29 carried out experiments on the Amazon benchmark released by Blitzer et al  ( BIBREF0 )  The dataset contains 4 different domains: Book (B), DVDs (D), Electronics (E), and Kitchen (K)  Following their experimental settings, we consider the binary classification task to predict whether a review is positive or negative on the target domain  Each domain consists of 1000 positive and 1000 negative reviews respectively  We also allow 4000 unlabeled reviews to be used for both the source and the target domains, of which the positive and negative reviews are balanced as well, following the settings in previous works  We construct 12 cross-domain sentiment classification tasks and split the labeled data in each domain into a training set of 1600 reviews and a test set of 400 reviews  The classifier is trained on the training set of the source domain and is evaluated on the test set of the target domain  The comparison results are shown in Table TABREF37  
Numerical Results of Figure 
Due to space limitation, we only show results in figures in the paper  All numerical numbers used for plotting Figure FIGREF17 are presented in Table TABREF38   We can observe that DAS-EM, DAS-SE, and DAS all achieve substantial improvements over baseline methods under different settings 
CNN Filter Analysis Full Results
As mentioned in Section SECREF36 , we conduct CNN filter analysis on NaiveNN, FANN, and DAS  For each method, we identify the top 10 most related filters for positive, negative, neutral sentiment labels respectively, and then represent each selected filter as a ranked list of trigrams with the highest activation values on it  Table TABREF39 , TABREF40 , TABREF41 in the following pages illustrate the trigrams from the target domain (beauty) captured by the selected filters learned on E INLINEFORM0 BT for each method 
We can observe that compared to NaiveNN and FANN, DAS is able to capture a more diverse set of relevant sentiment expressions on the target domain for each sentiment label  This observation is consistent with our motivation  Since NaiveNN, FANN and other baseline methods solely train the sentiment classifier on the source domain, the learned encoder is not able to produce discriminative features on the target domain  DAS addresses this problem by refining the classifier on the target domain with semi-supervised learning, and the overall objective forces the encoder to learn feature representations that are not only domain-invariant but also discriminative on both domains 

ground_truth: Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen

we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain, Book (BK), Electronics (E), Beauty (BT), and Music (M)