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. Do they employ their indexing-based method to create a sample of a QA Wikipedia dataset?
Context 1: Introduction
Question answering (QA) has been a blooming research field for the last decade  Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language  Three tasks have been proposed for selection-based QA  Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks  Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e g , IBM Watson, MIT Start) 
Several datasets have been released for selection-based QA  wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task  feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts  yang:15a introduced WikiQA for answer selection and triggering  jurczyk:16 created SelQA for large real-scale answer triggering  rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension  Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection 
These corpora make it possible to evaluate the robustness of statistical question answering learning  Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made  In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA  We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 )  We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 )  Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ) 
Intrinsic Analysis
Four publicly available corpora are selected for our analysis  These corpora are based on Wikipedia, so more comparable than the others, and have already been used for the evaluation of several QA systems 
WikiQA BIBREF6 comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles  The abstracts of these articles are then extracted to create answer candidates  The assumption is made that if many queries lead to the same article, it must contain the answer context; however, this assumption fails for some occasions, which makes this dataset more challenging  Since the existence of answer contexts is not guaranteed in this task, it is called answer triggering instead of answer selection 
SelQA BIBREF7 is a product of five annotation tasks through crowdsourcing  It consists of about 8K questions where a half of the questions are paraphrased from the other half, aiming to reduce contextual similarities between questions and answers  Each question is associated with a section in Wikipedia where the answer context is guaranteed, and also with five sections selected from the entire Wikipedia where the selection is made by the Lucene search engine  This second dataset does not assume the existence of the answer context, so can be used for the evaluation of answer triggering 
SQuAD BIBREF12 presents 107K+ crowdsourced questions on 536 Wikipedia articles, where the answer contexts are guaranteed to exist within the provided paragraph  It contains annotation of answer phrases as well as the pointers to the sentences including the answer phrases; thus, it can be used for both answer extraction and selection  This corpus also provides human accuracy on those questions, setting up a reasonable upper bound for machines  To avoid overfitting, the evaluation set is not publicly available although system outputs can be evaluated by their provided script 
InfoboxQA BIBREF13 gives 15K+ questions based on the infoboxes from 150 articles in Wikipedia  Each question is crowdsourced and associated with an infobox, where each line of the infobox is considered an answer candidate  This corpus emphasizes the gravity of infoboxes, which summary arguably the most commonly asked information about those articles  Although the nature of this corpus is different from the others, it can also be used to evaluate answer selection 
Analysis
All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively  SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times  On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts  Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes 
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate  SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes  It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries  InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes  InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively  In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close 
Fig  FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons  Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on 
Fig  FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a  Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models 
Answer Retrieval
This section describes another selection-based QA task, called answer retrieval, that finds the answer context from a larger dataset, the entire Wikipedia  SQuAD provides no mapping of the answer contexts to Wikipedia, whereas WikiQA and SelQA provide mappings; however, their data do not come from the same version of Wikipedia  We propose an automatic way of mapping the answer contexts from all corpora to the same version of Wikipeda so they can be coherently used for answer retrieval 
Each paragraph in Wikipedia is first indexed by Lucene using {1,2,3}-grams, where the paragraphs are separated by WikiExtractor and segmented by NLP4J (28 7M+ paragraphs are indexed)  Each answer sentence from the corpora in Table TABREF3 is then queried to Lucene, and the top-5 ranked paragraphs are retrieved  The cosine similarity between each sentence in these paragraphs and the answer sentence is measured for INLINEFORM0 -grams, say INLINEFORM1   A weight is assigned to each INLINEFORM2 -gram score, say INLINEFORM3 , and the weighted sum is measured: INLINEFORM4   The fixed weights of INLINEFORM5 are used for our experiments, which can be improved 
If there exists a sentence whose INLINEFORM0 , the paragraph consisting of that sentence is considered the silver-standard answer passage  Table TABREF3 shows how robust these silver-standard passages are based on human judgement ( INLINEFORM1 ) and how many passages are collected ( INLINEFORM2 ) for INLINEFORM3 , where the human judgement is performed on 50 random samples for each case  For answer retrieval, a dataset is created by INLINEFORM4 , which gives INLINEFORM5 accuracy and INLINEFORM6 coverage, respectively  Finally, each question is queried to Lucene and the top- INLINEFORM7 paragraphs are retrieved from the entire Wikipedia  If the answer sentence exists within those retrieved paragraphs according to the silver-standard, it is considered correct 
Finding a paragraph that includes the answer context out of the entire Wikipedia is an extremely difficult task (128 7M)  The last row of Table TABREF3 shows results from answer retrieval  Given INLINEFORM0 , SelQA and SQuAD show about 34% and 35% accuracy, which are reasonable  However, WikiQA shows a significantly lower accuracy of 12 47%; this is because the questions in WikiQA is about twice shorter than the questions in the other corpora such that not enough lexicons can be extracted from these questions for the Lucene search 
Answer Selection
Answer selection is evaluated by two metrics, mean average precision (MAP) and mean reciprocal rank (MRR)  The bigram CNN introduced by yu:14a is used to generate all the results in Table TABREF11 , where models are trained on either single or combined datasets  Clearly, the questions in WikiQA are the most challenging, and adding more training data from the other corpora hurts accuracy due to the uniqueness of query-based questions in this corpus  The best model is achieved by training on W+S+Q for SelQA; adding InfoboxQA hurts accuracy for SelQA although it gives a marginal gain for SQuAD  Just like WikiQA, InfoboxQA performs the best when it is trained on only itself  From our analysis, we suggest that to use models trained on WikiQA and InfoboxQA for short query-like questions, whereas to use ones trained on SelQA and SQuAD for long natural questions 
Answer Triggering
The results of INLINEFORM0 from the answer retrieval task in Section SECREF13 are used to create the datasets for answer triggering, where about 65% of the questions are not expected to find their answer contexts from the provided paragraphs for SelQA and SQuAD and 87 5% are not expected for WikiQA  Answer triggering is evaluated by the F1 scores as presented in Table TABREF11 , where three corpora are cross validated  The results on WikiQA are pretty low as expected from the poor accuracy on the answer retrieval task  Training on SelQA gives the best models for both WikiQA and SelQA  Training on SQuAD gives the best model for SQuAD although the model trained on SelQA is comparable  Since the answer triggering datasets are about 5 times larger than the answer selection datasets, it is computationally too expensive to combine all data for training  We plan to find a strong machine to perform this experiment in near future 
Related work
Lately, several deep learning approaches have been proposed for question answering  yu:14a presented a CNN model that recognizes the semantic similarity between two sentences  wang-nyberg:2015:ACL-IJCNLP presented a stacked bidirectional LSTM approach to read words in sequence, then outputs their similarity scores  feng:15a applied a general deep learning framework to non-factoid question answering  santos:16a introduced an attentive pooling mechanism that led to further improvements in selection-based QA 
Conclusion
We present a comprehensive comparison study of the existing corpora for selection-based question answering  Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora  Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning  Additionally, we create a silver-standard dataset for answer retrieval and triggering, which will be publicly available  In the future, we will explore different ways of improving the quality of our silver-standard datasets by fine-tuning the hyper-parameters 
Context 3: Introduction
Speech-to-Text translation (ST) is essential for a wide range of scenarios: for example in emergency calls, where agents have to respond emergent requests in a foreign language BIBREF0; or in online courses, where audiences and speakers use different languages BIBREF1  To tackle this problem, existing approaches can be categorized into cascaded method BIBREF2, BIBREF3, where a machine translation (MT) model translates outputs of an automatic speech recognition (ASR) system into target language, and end-to-end method BIBREF4, BIBREF5, where a single model learns acoustic frames to target word sequence mappings in one step towards the final objective of interest  Although the cascaded model remains the dominant approach due to its better performance, the end-to-end method becomes more and more popular because it has lower latency by avoiding inferences with two models and rectifies the error propagation in theory 
Since it is hard to obtain a large-scale ST dataset, multi-task learning BIBREF5, BIBREF6 and pre-training techniques BIBREF7 have been applied to end-to-end ST model to leverage large-scale datasets of ASR and MT  A common practice is to pre-train two encoder-decoder models for ASR and MT respectively, and then initialize the ST model with the encoder of the ASR model and the decoder of the MT model  Subsequently, the ST model is optimized with the multi-task learning by weighing the losses of ASR, MT, and ST  This approach, however, causes a huge gap between pre-training and fine-tuning, which are summarized into three folds:
Subnet Waste: The ST system just reuses the ASR encoder and the MT decoder, while discards other pre-trained subnets, such as the MT encoder  Consequently, valuable semantic information captured by the MT encoder cannot be inherited by the final ST system 
Role Mismatch: The speech encoder plays different roles in pre-training and fine-tuning  The encoder is a pure acoustic model in pre-training, while it has to extract semantic and linguistic features additionally in fine-tuning, which significantly increases the learning difficulty 
Non-pre-trained Attention Module: Previous work BIBREF6 trains attention modules for ASR, MT and ST respectively, hence, the attention module of ST does not benefit from the pre-training 
To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN), which is able to reuse all subnets in pre-training, keep the roles of subnets consistent, and pre-train the attention module  Concretely, the TCEN consists of three components, a speech encoder, a text encoder, and a target text decoder  Different from the previous work that pre-trains an encoder-decoder based ASR model, we only pre-train an ASR encoder by optimizing the Connectionist Temporal Classification (CTC) BIBREF8 objective function  In this way, the additional decoder of ASR is not required while keeping the ability to read acoustic features into the source language space by the speech encoder  Besides, the text encoder and decoder can be pre-trained on a large MT dataset  After that, we employ common used multi-task learning method to jointly learn ASR, MT and ST tasks 
Compared to prior works, the encoder of TCEN is a concatenation of an ASR encoder and an MT encoder and our model does not have an ASR decoder, so the subnet waste issue is solved  Furthermore, the two encoders work at tandem, disentangling acoustic feature extraction and linguistic feature extraction, ensuring the role consistency between pre-training and fine-tuning  Moreover, we reuse the pre-trained MT attention module in ST, so we can leverage the alignment information learned in pre-training 
Since the text encoder consumes word embeddings of plausible texts in MT task but uses speech encoder outputs in ST task, another question is how one guarantees the speech encoder outputs are consistent with the word embeddings  We further modify our model to achieve semantic consistency and length consistency  Specifically, (1) the projection matrix at the CTC classification layer for ASR is shared with the word embedding matrix, ensuring that they are mapped to the same latent space, and (2) the length of the speech encoder output is proportional to the length of the input frame, so it is much longer than a natural sentence  To bridge the length gap, source sentences in MT are lengthened by adding word repetitions and blank tokens to mimic the CTC output sequences 
We conduct comprehensive experiments on the IWSLT18 speech translation benchmark BIBREF1, demonstrating the effectiveness of each component  Our model is significantly better than previous methods by 3 6 and 2 2 BLEU scores for the subword-level decoding and character-level decoding strategies, respectively 
Our contributions are three-folds: 1) we shed light on why previous ST models cannot sufficiently utilize the knowledge learned from the pre-training process; 2) we propose a new ST model, which alleviates shortcomings in existing methods; and 3) we empirically evaluate the proposed model on a large-scale public dataset 
Background ::: Problem Formulation
End-to-end speech translation aims to translate a piece of audio into a target-language translation in one step  The raw speech signals are usually converted to sequences of acoustic features, e g  Mel filterbank features  Here, we define the speech feature sequence as $\mathbf {x} = (x_1, \cdots , x_{T_x})$ The transcription and translation sequences are denoted as $\mathbf {y^{s}} = (y_1^{s}, \cdots , y_{T_s}^{s})$, and $\mathbf {y^{t}} = (y_1^{t}, \cdots , y_{T_t}^{t})$ repectively  Each symbol in $\mathbf {y^{s}}$ or $\mathbf {y^{t}}$ is an integer index of the symbol in a vocabulary $V_{src}$ or $V_{trg}$ respectively (e g  $y^s_i=k, k\in [0, |V_{src}|-1]$)  In this work, we suppose that an ASR dataset, an MT dataset, and a ST dataset are available, denoted as $\mathcal {A} = \lbrace (\mathbf {x_i}, \mathbf {y^{s}_i})\rbrace _{i=0}^I$, $\mathcal {M} =\lbrace (\mathbf {y^{s}_j}, \mathbf {y^{t}_j})\rbrace _{j=0}^J$ and $ \mathcal {S} =\lbrace (\mathbf {x_l}, \mathbf {y^{t}_l})\rbrace _{l=0}^L$ respectively  Given a new piece of audio $\mathbf {x}$, our goal is to learn an end to end model to generate a translation sentence $\mathbf {y^{t}}$ without generating an intermediate result $\mathbf {y^{s}}$ 
Background ::: Multi-Task Learning and Pre-training for ST
To leverage large scale ASR and MT data, multi-task learning and pre-training techniques are widely employed to improve the ST system  As shown in Figure FIGREF4, there are three popular multi-task strategies for ST, including 1) one-to-many setting, in which a speech encoder is shared between ASR and ST tasks; 2) many-to-one setting in which a decoder is shared between MT and ST tasks; and 3) many-to-many setting where both the encoder and decoder are shared 
A many-to-many multi-task model contains two encoders as well as two decoders  It can be jointly trained on ASR, MT, and ST tasks  As the attention module is task-specific, three attentions are defined 
Usually, the size of $\mathcal {A}$ and $\mathcal {M}$ is much larger than $\mathcal {S}$  Therefore, the common training practice is to pre-train the model on ASR and MT tasks and then fine-tune it with a multi-task learning manner  However, as aforementioned, this method suffers from subnet waste, role mismatch and non-pre-trained attention issues, which severely limits the end-to-end ST performance 
Our method
In this section, we first introduce the architecture of TCEN, which consists of two encoders connected in tandem, and one decoder with an attention module  Then we give the pre-training and fine-tuning strategy for TCEN  Finally, we propose our solutions for semantic and length inconsistency problems, which are caused by multi-task learning 
Our method ::: TCEN Architecture
Figure FIGREF5 sketches the overall architecture of TCEN, including a speech encoder $enc_s$, a text encoder $enc_t$ and a decoder $dec$ with an attention module $att$  During training, the $enc_s$ acts like an acoustic model which reads the input $\mathbf {x}$ to word or subword representations $\mathbf {h^s}$, then $enc_t$ learns high-level linguistic knowledge into hidden representations $\mathbf {h^t}$  Finally, the $dec$ defines a distribution probability over target words  The advantage of our architecture is that two encoders disentangle acoustic feature extraction and linguistic feature extraction, making sure that valuable knowledge learned from ASR and MT tasks can be effectively leveraged for ST training  Besides, every module in pre-training can be utilized in fine-tuning, alleviating the subnet waste problem 
Follow BIBREF9 inaguma2018speech, we use CNN-BiLSTM architecture to build our model  Specifically, the input features $\mathbf {x}$ are organized as a sequence of feature vectors in length $T_x$  Then, $\mathbf {x}$ is passed into a stack of two convolutional layers followed by max-pooling:
where $\mathbf {v}^{(l-1)}$ is feature maps in last layer and $\mathbf {W}^{(l)}$ is the filter  The max-pooling layers downsample the sequence in length by a total factor of four  The down-sampled feature sequence is further fed into a stack of five bidirectional $d$-dimensional LSTM layers:
where $[;]$ denotes the vector concatenation  The final output representation from the speech encoder is denoted as $\mathbf {h^s}=(h^s_1, \cdots , h^s_{\frac{T_x}{4}})$, where $h_i^s \in \mathbb {R}^d$ 
The text encoder $enc_t$ consists of two bidirectional LSTM layers  In ST task, $enc_t$ accepts speech encoder output $\mathbf {h}^s$ as input  While in MT, $enc_t$ consumes the word embedding representation $\mathbf {e^s}$ derived from $\mathbf {y^s}$, where each element $e^s_i$ is computed by choosing the $y_i^s$-th vector from the source embedding matrix $W_{E^s}$  The goal of $enc_t$ is to extract high-level linguistic features like syntactic features or semantic features from lower level subword representations $\mathbf {h}^s$ or $\mathbf {e}^s$  Since $\mathbf {h}^s$ and $\mathbf {e}^s$ belong to different latent space and have different lengths, there remain semantic and length inconsistency problems  We will provide our solutions in Section SECREF21  The output sequence of $enc_t$ is denoted as $\mathbf {h}^t$ 
The decoder is defined as two unidirectional LSTM layers with an additive attention $att$  It predicts target sequence $\mathbf {y^{t}}$ by estimating conditional probability $P(\mathbf {y^{t}}|\mathbf {x})$:
Here, $z_k$ is the the hidden state of the deocder RNN at $k$ step and $c_k$ is a time-dependent context vector computed by the attention $att$ 
Our method ::: Training Procedure
Following previous work, we split the training procedure to pre-training and fine-tuning stages  In pre-training stage, the speech encoder $enc_s$ is trained towards CTC objective using dataset $\mathcal {A}$, while the text encoder $enc_t$ and the decoder $dec$ are trained on MT dataset $\mathcal {M}$  In fine-tuning stage, we jointly train the model on ASR, MT, and ST tasks 
Our method ::: Training Procedure ::: Pre-training
To sufficiently utilize the large dataset $\mathcal {A}$ and $\mathcal {M}$, the model is pre-trained on CTC-based ASR task and MT task in the pre-training stage 
For ASR task, in order to get rid of the requirement for decoder and enable the $enc_s$ to generate subword representation, we leverage connectionist temporal classification (CTC) BIBREF8 loss to train the speech encoder 
Given an input $\mathbf {x}$, $enc_s$ emits a sequence of hidden vectors $\mathbf {h^s}$, then a softmax classification layer predicts a CTC path $\mathbf {\pi }$, where $\pi _t \in V_{src} \cup $ {`-'} is the observing label at particular RNN step $t$, and `-' is the blank token representing no observed labels:
where $W_{ctc} \in \mathbb {R}^{d \times (|V_{src}|+1)}$ is the weight matrix in the classification layer and $T$ is the total length of encoder RNN 
A legal CTC path $\mathbf {\pi }$ is a variation of the source transcription $\mathbf {y}^s$ by allowing occurrences of blank tokens and repetitions, as shown in Table TABREF14

ground_truth: Yes

No

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 much higher quality is the resulting annotated data?
Context 2: Introduction
Assembling training corpora of annotated natural language examples in specialized domains such as biomedicine poses considerable challenges  Experts with the requisite domain knowledge to perform high-quality annotation tend to be expensive, while lay annotators may not have the necessary knowledge to provide high-quality annotations  A practical approach for collecting a sufficiently large corpus would be to use crowdsourcing platforms like Amazon Mechanical Turk (MTurk)  However, crowd workers in general are likely to provide noisy annotations BIBREF0 , BIBREF1 , BIBREF2 , an issue exacerbated by the technical nature of specialized content  Some of this noise may reflect worker quality and can be modeled BIBREF0 , BIBREF1 , BIBREF3 , BIBREF4 , but for some instances lay people may simply lack the domain knowledge to provide useful annotation 
In this paper we report experiments on the EBM-NLP corpus comprising crowdsourced annotations of medical literature BIBREF5   We operationalize the concept of annotation difficulty and show how it can be exploited during training to improve information extraction models  We then obtain expert annotations for the abstracts predicted to be most difficult, as well as for a similar number of randomly selected abstracts  The annotation of highly specialized data and the use of lay and expert annotators allow us to examine the following key questions related to lay and expert annotations in specialized domains:
Can we predict item difficulty? We define a training instance as difficult if a lay annotator or an automated model disagree on its labeling  We show that difficulty can be predicted, and that it is distinct from inter-annotator agreement  Further, such predictions can be used during training to improve information extraction models 
Are there systematic differences between expert and lay annotations? We observe decidedly lower agreement between lay workers as compared to domain experts  Lay annotations have high precision but low recall with respect to expert annotations in the new data that we collected  More generally, we expect lay annotations to be lower quality, which may translate to lower precision, recall, or both, compared to expert annotations  Can one rely solely on lay annotations? Reasonable models can be trained using lay annotations alone, but similar performance can be achieved using markedly less expert data  This suggests that the optimal ratio of expert to crowd annotations for specialized tasks will depend on the cost and availability of domain experts  Expert annotations are preferable whenever its collection is practical  But in real-world settings, a combination of expert and lay annotations is better than using lay data alone 
Does it matter what data is annotated by experts? We demonstrate that a system trained on combined data achieves better predictive performance when experts annotate difficult examples rather than instances selected at i i d  random 
Our contributions in this work are summarized as follows  We define a task difficulty prediction task and show how this is related to, but distinct from, inter-worker agreement  We introduce a new model for difficulty prediction combining learned representations induced via a pre-trained `universal' sentence encoder BIBREF6 , and a sentence encoder learned from scratch for this task  We show that predicting annotation difficulty can be used to improve the task routing and model performance for a biomedical information extraction task  Our results open up a new direction for ensuring corpus quality  We believe that item difficulty prediction will likely be useful in other, non-specialized tasks as well, and that the most effective data collection in specialized domains requires research addressing the fundamental questions we examine here 
Related Work
Crowdsourcing annotation is now a well-studied problem BIBREF7 , BIBREF0 , BIBREF1 , BIBREF2   Due to the noise inherent in such annotations, there have also been considerable efforts to develop aggregation models that minimize noise BIBREF0 , BIBREF1 , BIBREF3 , BIBREF4  
There are also several surveys of crowdsourcing in biomedicine specifically BIBREF8 , BIBREF9 , BIBREF10   Some work in this space has contrasted model performance achieved using expert vs  crowd annotated training data BIBREF11 , BIBREF12 , BIBREF13   Dumitrache et al  Dumitrache:2018:CGT:3232718 3152889 concluded that performance is similar under these supervision types, finding no clear advantage from using expert annotators  This differs from our findings, perhaps owing to differences in design  The experts we used already hold advanced medical degrees, for instance, while those in prior work were medical students  Furthermore, the task considered here would appear to be of greater difficulty: even a system trained on $\sim $ 5k instances performs reasonably, but far from perfect  By contrast, in some of the prior work where experts and crowd annotations were deemed equivalent, a classifier trained on 300 examples can achieve very high accuracy BIBREF12  
More relevant to this paper, prior work has investigated methods for `task routing' in active learning scenarios in which supervision is provided by heterogeneous labelers with varying levels of expertise BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF14   The related question of whether effort is better spent collecting additional annotations for already labeled (but potentially noisily so) examples or novel instances has also been addressed BIBREF18   What distinguishes the work here is our focus on providing an operational definition of instance difficulty, showing that this can be predicted, and then using this to inform task routing 
Application Domain
Our specific application concerns annotating abstracts of articles that describe the conduct and results of randomized controlled trials (RCTs)  Experimentation in this domain has become easy with the recent release of the EBM-NLP BIBREF5 corpus, which includes a reasonably large training dataset annotated via crowdsourcing, and a modest test set labeled by individuals with advanced medical training  More specifically, the training set comprises 4,741 medical article abstracts with crowdsourced annotations indicating snippets (sequences) that describe the Participants (p), Interventions (i), and Outcome (o) elements of the respective RCT, and the test set is composed of 191 abstracts with p, i, o sequence annotations from three medical experts 
Table 1 shows an example of difficult and easy examples according to our definition of difficulty  The underlined text demarcates the (consensus) reference label provided by domain experts  In the difficult examples, crowd workers marked text distinct from these reference annotations; whereas in the easy cases they reproduced them with reasonable fidelity  The difficult sentences usually exhibit complicated structure and feature jargon 
An abstract may contain some `easy' and some `difficult' sentences  We thus perform our analysis at the sentence level  We split abstracts into sentences using spaCy  We excluded sentences that comprise fewer than two tokens, as these are likely an artifact of errors in sentence splitting  In total, this resulted in 57,505 and 2,428 sentences in the train and test set abstracts, respectively 
Quantifying Task Difficulty
The test set includes annotations from both crowd workers and domain experts  We treat the latter as ground truth and then define the difficulty of sentences in terms of the observed agreement between expert and lay annotators  Formally, for annotation task $t$ and instance $i$ :
$$\text{Difficulty}_{ti} = \frac{\sum _{j=1}^n{f(\text{label}_{ij}, y_i})}{n}$$   (Eq  3)
where $f$ is a scoring function that measures the quality of the label from worker $j$ for sentence $i$ , as compared to a ground truth annotation, $y_i$   The difficulty score of sentence $i$ is taken as an average over the scores for all $n$ layworkers  We use Spearmans' correlation coefficient as a scoring function  Specifically, for each sentence we create two vectors comprising counts of how many times each token was annotated by crowd and expert workers, respectively, and calculate the correlation between these  Sentences with no labels are treated as maximally easy; those with only either crowd worker or expert label(s) are assumed maximally difficult 
The training set contains only crowdsourced annotations  To label the training data, we use a 10-fold validation like setting  We iteratively retrain the LSTM-CRF-Pattern sequence tagger of Patel et al  patel2018syntactic on 9 folds of the training data and use that trained model to predict labels for the 10th  In this way we obtain predictions on the full training set  We then use predicted spans as proxy `ground truth' annotations to calculate the difficulty score of sentences as described above; we normalize these to the [ $0, 1$ ] interval  We validate this approximation by comparing the proxy scores against reference scores over the test set, the Pearson's correlation coefficients are 0 57 for Population, 0 71 for Intervention and 0 68 for Outcome 
There exist many sentences that contain neither manual nor predicted annotations  We treat these as maximally easy sentences (with difficulty scores of 0)  Such sentences comprise 51%, 42% and 36% for Population, Interventions and Outcomes data respectively, indicating that it is easier to identify sentences that have no Population spans, but harder to identify sentences that have no Interventions or Outcomes spans  This is intuitive as descriptions of the latter two tend to be more technical and dense with medical jargon 
We show the distribution of the automatically labeled scores for sentences that do contain spans in Figure 1   The mean of the Population (p) sentence scores is significantly lower than that for other types of sentences (i and o), again indicating that they are easier on average to annotate  This aligns with a previous finding that annotating Interventions and Outcomes is more difficult than annotating Participants BIBREF5  
Many sentences contain spans tagged by the LSTM-CRF-Pattern model, but missed by all crowd workers, resulting in a maximally difficult score (1)  Inspection of such sentences revealed that some are truly difficult examples, but others are tagging model errors  In either case, such sentences have confused workers and/or the model, and so we retain them all as `difficult' sentences 
Content describing the p, i and o, respectively, is quite different  As such, one sentence usually contains (at most) only one of these three content types  We thus treat difficulty prediction for the respective label types as separate tasks 
Difficulty is not Worker Agreement
Our definition of difficulty is derived from agreement between expert and crowd annotations for the test data, and agreement between a predictive model and crowd annotations in the training data  It is reasonable to ask if these measures are related to inter-annotator agreement, a metric often used in language technology research to identify ambiguous or difficult items  Here we explicitly verify that our definition of difficulty only weakly correlates with inter-annotator agreement 
We calculate inter-worker agreement between crowd and expert annotators using Spearman's correlation coefficient  As shown in Table 2 , average agreement between domain experts are considerably higher than agreements between crowd workers for all three label types  This is a clear indication that the crowd annotations are noisier 
Furthermore, we compare the correlation between inter-annotator agreement and difficulty scores in the training data  Given that the majority of sentences do not contain a PICO span, we only include in these calculations those that contain a reference label  Pearson's r are 0 34, 0 30 and 0 31 for p, i and o, respectively, confirming that inter-worker agreement and our proposed difficulty score are quite distinct 
Predicting Annotation Difficulty
We treat difficulty prediction as a regression problem, and propose and evaluate neural model variants for the task  We first train RNN BIBREF19 and CNN BIBREF20 models 
We also use the universal sentence encoder (USE) BIBREF6 to induce sentence representations, and train a model using these as features  Following BIBREF6 , we then experiment with an ensemble model that combines the `universal' and task-specific representations to predict annotation difficulty  We expect these universal embeddings to capture general, high-level semantics, and the task specific representations to capture more granular information  Figure 2 depicts the model architecture  Sentences are fed into both the universal sentence encoder and, separately, a task specific neural encoder, yielding two representations  We concatenate these and pass the combined vector to the regression layer 
Experimental Setup and Results
We trained models for each label type separately  Word embeddings were initialized to 300d GloVe vectors BIBREF21 trained on common crawl data; these are fine-tuned during training  We used the Adam optimizer BIBREF22 with learning rate and decay set to 0 001 and 0 99, respectively  We used batch sizes of 16 
We used the large version of the universal sentence encoder with a transformer BIBREF23   We did not update the pretrained sentence encoder parameters during training  All hyperparamaters for all models (including hidden layers, hidden sizes, and dropout) were tuned using Vizier BIBREF24 via 10-fold cross validation on the training set maximizing for F1 
As a baseline, we also trained a linear Support-Vector Regression BIBREF25 model on $n$ -gram features ( $n$ ranges from 1 to 3) 
Table 3 reports Pearson correlation coefficients between the predictions with each of the neural models and the ground truth difficulty scores  Rows 1-4 correspond to individual models, and row 5 reports the ensemble performance  Columns correspond to label type  Results from all models outperform the baseline SVR model: Pearson's correlation coefficients range from 0 550 to 0 622  The regression correlations are the lowest 
The RNN model realizes the strongest performance among the stand-alone (non-ensemble) models, outperforming variants that exploit CNN and USE representations  Combining the RNN and USE further improves results  We hypothesize that this is due to complementary sentence information encoded in universal representations 
For all models, correlations for Intervention and Outcomes are higher than for Population, which is expected given the difficulty distributions in Figure 1   In these, the sentences are more uniformly distributed, with a fair number of difficult and easier sentences  By contrast, in Population there are a greater number of easy sentences and considerably fewer difficult sentences, which makes the difficulty ranking task particularly challenging 
Better IE with Difficulty Prediction
We next present experiments in which we attempt to use the predicted difficulty during training to improve models for information extraction of descriptions of Population, Interventions and Outcomes from medical article abstracts  We investigate two uses: (1) simply removing the most difficult sentences from the training set, and, (2) re-weighting the most difficult sentences 
We again use LSTM-CRF-Pattern as the base model and experimenting on the EBM-NLP corpus BIBREF5   This is trained on either (1) the training set with difficult sentences removed, or (2) the full training set but with instances re-weighted in proportion to their predicted difficulty score  Following BIBREF5 , we use the Adam optimizer with learning rate of 0 001, decay 0 9, batch size 20 and dropout 0 5  We use pretrained 200d GloVe vectors BIBREF21 to initialize word embeddings, and use 100d hidden char representations  Each word is thus represented with 300 dimensions in total  The hidden size is 100 for the LSTM in the character representation component, and 200 for the LSTM in the information extraction component  We train for 15 epochs, saving parameters that achieve the best F1 score on a nested development set 
Removing Difficult Examples
We first evaluate changes in performance induced by training the sequence labeling model using less data by removing difficult sentences prior to training  The hypothesis here is that these difficult instances are likely to introduce more noise than signal  We used a cross-fold approach to predict sentence difficulties, training on 9/10ths of the data and scoring the remaining 1/10th at a time  We then sorted sentences by predicted difficulty scores, and experimented with removing increasing numbers of these (in order of difficulty) prior to training the LSTM-CRF-Pattern model 
Figure 3 shows the results achieved by the LSTM-CRF-Pattern model after discarding increasing amounts of the training data: the $x$ and $y$ axes correspond to the the percentage of data removed and F1 scores, respectively  We contrast removing sentences predicted to be difficult with removing them (a) randomly (i i d ), and, (b) in inverse order of predicted inter-annotator agreement  The agreement prediction model is trained exactly the same like difficult prediction model, with simply changing the difficult score to annotation agreement  F1 scores actually improve (marginally) when we remove the most difficult sentences, up until we drop 4% of the data for Population and Interventions, and 6% for Outcomes  Removing training points at i i d  random degrades performance, as expected  Removing sentences in order of disagreement seems to have similar effect as removing them by difficulty score when removing small amount of the data, but the F1 scores drop much faster when removing more data  These findings indicate that sentences predicted to be difficult are indeed noisy, to the extent that they do not seem to provide the model useful signal 
Re-weighting by Difficulty
We showed above that removing a small number of the most difficult sentences does not harm, and in fact modestly improves, medical IE model performance  However, using the available data we are unable to test if this will be useful in practice, as we would need additional data to determine how many difficult sentences should be dropped 
We instead explore an alternative, practical means of exploiting difficulty predictions: we re-weight sentences during training inversely to their predicted difficulty  Formally, we weight sentence $i$ with difficulty scores above $\tau $ according to: $1-a\cdot (d_i-\tau )/(1-\tau )$ , where $d_i$ is the difficulty score for sentence $i$ , and $a$ is a parameter codifying the minimum weight value  We set $\tau $ to 0 8 so as to only re-weight sentences with difficulty in the top 20th percentile, and we set $a$ to 0 5  The re-weighting is equivalent to down-sampling the difficult sentences  LSTM-CRF-Pattern is our base model 
Table 4 reports the precision, recall and F1 achieved both with and without sentence re-weighting  Re-weighting improves all metrics modestly but consistently  All F1 differences are statistically significant under a sign test ( $p<0 01$ )  The model with best precision is different for Patient, Intervention and Outcome labels  However re-weighting by difficulty does consistently yield the best recall for all three extraction types, with the most notable improvement for i and o, where recall improved by 10 percentage points  This performance increase translated to improvements in F1 across all types, as compared to the base model and to re-weighting by agreement 
Involving Expert Annotators
The preceding experiments demonstrate that re-weighting difficult sentences annotated by the crowd generally improves the extraction models  Presumably the performance is influenced by the annotation quality 
We now examine the possibility that the higher quality and more consistent annotations of domain experts on the difficult instances will benefit the extraction model  This simulates an annotation strategy in which we route difficult instances to domain experts and easier ones to crowd annotators  We also contrast the value of difficult data to that of an i i d  random sample of the same size, both annotated by experts 
Expert annotations of Random and Difficult Instances
We re-annotate by experts a subset of most difficult instances and the same number of random instances  As collecting annotations from experts is slow and expensive, we only re-annotate the difficult instances for the interventions extraction task  We re-annotate the abstracts which cover the sentences with predicted difficulty scores in the top 5 percentile  We rank the abstracts from the training set by the count of difficult sentences, and re-annotate the abstracts that contain the most difficult sentences  Constrained by time and budget, we select only 2000 abstracts for re-annotation; 1000 of these are top-ranked, and 1000 are randomly sampled  This re-annotation cost $3,000  We have released the new annotation data at: https://github com/bepnye/EBM-NLP 
Following BIBREF5 , we recruited five medical experts via Up-work with advanced medical training and strong technical reading/writing skills  The expert annotator were asked to read the entire abstract and highlight, using the BRAT toolkit BIBREF26 , all spans describing medical Interventions  Each abstract is only annotated by one expert  We examined 30 re-annotated abstracts to ensure the annotation quality before hiring the annotator 
Table 5 presents the results of LSTM-CRF-Pattern model trained on the reannotated difficult subset and the random subset  The first two rows show the results for models trained with expert annotations  The model trained on random data has a slightly better F1 than that trained on the same amount of difficult data  The model trained on random data has higher precision but lower recall 
Rows 3 and 4 list the results for models trained on the same data but with crowd annotation  Models trained with expert-annotated data are clearly superior to those trained with crowd labels with respect to F1, indicating that the experts produced higher quality annotations  For crowdsourced annotations, training the model with data sampled at i i d  random achieves 2% higher F1 than when difficult instances are used  When expert annotations are used, this difference is less than 1%  This trend in performance may be explained by differences in annotation quality: the randomly sampled set was more consistently annotated by both experts and crowd because the difficult set is harder  However, in both cases expert annotations are better, with a bigger difference between the expert and crowd models on the difficult set 
The last row is the model trained on all 5k abstracts with crowd annotations  Its F1 score is lower than either expert model trained on only 20% of data, suggesting that expert annotations should be collected whenever possible  Again the crowd model on complete data has higher precision than expert models but its recall is much lower 
Routing To Experts or Crowd
So far a system was trained on one type of data, either labeled by crowd or experts  We now examine the performance of a system trained on data that was routed to either experts or crowd annotators depending on their predicted difficult  Given the results presented so far mixing annotators may be beneficial given their respective trade-offs of precision and recall  We use the annotations from experts for an abstract if it exists otherwise use crowd annotations  The results are presented in Table 6  
Rows 1 and 2 repeat the performance of the models trained on difficult subset and random subset with expert annotations only respectively  The third row is the model trained by combining difficult and random subsets with expert annotations  There are around 250 abstracts in the overlap of these two sets, so there are total 1 75k abstracts used for training the D+R model  Rows 4 to 6 are the models trained on all 5k abstracts with mixed annotations, where Other means the rest of the abstracts with crowd annotation only 
The results show adding more training data with crowd annotation still improves at least 1 point F1 score in all three extraction tasks  The improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3 5 F1 score, much larger than when a random set of expert annotations are added  The model trained with re-annotating the difficult subset (D+Other) also outperforms the model with re-annotating the random subset (R+Other) by 2 points in F1  The model trained with re-annotating both of difficult and random subsets (D+R+Other), however, achieves only marginally higher F1 than the model trained with the re-annotated difficult subset (D+Other)  In sum, the results clearly indicate that mixing expert and crowd annotations leads to better models than using solely crowd data, and better than using expert data alone  More importantly, there is greater gain in performance when instances are routed according to difficulty, as compared to randomly selecting the data for expert annotators  These findings align with our motivating hypothesis that annotation quality for difficult instances is important for final model performance  They also indicate that mixing annotations from expert and crowd could be an effective way to achieve acceptable model performance given a limited budget 
How Many Expert Annotations?
We established that crowd annotation are still useful in supplementing expert annotations for medical IE  Obtaining expert annotations for the one thousand most difficult instances greatly improved the model performance  However the choice of how many difficult instances to annotate was an uninformed choice  Here we check if less expert data would have yielded similar gains  Future work will need to address how best to choose this parameter for a routing system 
We simulate a routing scenario in which we send consecutive batches of the most difficult examples to the experts for annotation  We track changes in performance as we increase the number of most-difficult-articles sent to domain experts  As shown in Figure 4 , adding expert annotations for difficult articles consistently increases F1 scores  The performance gain is mostly from increased recall; the precision changes only a bit with higher quality annotation  This observation implies that crowd workers often fail to mark target tokens, but do not tend to produce large numbers of false positives  We suspect such failures to identify relevant spans/tokens are due to insufficient domain knowledge possessed by crowd workers 
The F1 score achieved after re-annotating the 600 most-difficult articles reaches 68 1%, which is close to the performance when re-annotating 1000 random articles  This demonstrates the effectiveness of recognizing difficult instances  The trend when we use up all expert data is still upward, so adding even more expert data is likely to further improve performance  Unfortunately we exhausted our budget and were not able to obtain additional expert annotations  It is likely that as the size of the expert annotations increases, the value of crowd annotations will diminish  This investigation is left for future work 
Conclusions
We have introduced the task of predicting annotation difficulty for biomedical information extraction (IE)  We trained neural models using different learned representations to score texts in terms of their difficulty  Results from all models were strong with Pearson’s correlation coefficients higher than 0 45 in almost all evaluations, indicating the feasibility of this task  An ensemble model combining universal and task specific feature sentence vectors yielded the best results 
Experiments on biomedical IE tasks show that removing up to $\sim $ 10% of the sentences predicted to be most difficult did not decrease model performance, and that re-weighting sentences inversely to their difficulty score during training improves predictive performance  Simulations in which difficult examples are routed to experts and other instances to crowd annotators yields the best results, outperforming the strategy of randomly selecting data for expert annotation, and substantially improving upon the approach of relying exclusively on crowd annotations  In future work, routing strategies based on instance difficulty could be further investigated for budget-quality trade-off 
Acknowledgements
This work has been partially supported by NSF1748771 grant  Wallace was support in part by NIH/NLM R01LM012086 
Context 3:  For each transcription $\mathbf {y}$, there exist many legal CTC paths in length $T$  The CTC objective trains the model to maximize the probability of observing the golden sequence $\mathbf {y}^s$, which is calculated by summing the probabilities of all possible legal paths:
where $\Phi _T(y)$ is the set of all legal CTC paths for sequence $\mathbf {y}$ with length $T$  The loss can be easily computed using forward-backward algorithm  More details about CTC are provided in supplementary material 
For MT task, we use the cross-entropy loss as the training objective  During training, $\mathbf {y^s}$ is converted to embedding vectors $\mathbf {e^s}$ through embedding layer $W_{E^s}$, then $enc_t$ consumes $\mathbf {e^s}$ and pass the output $\mathbf {h^t}$ to decoder  The objective function is defined as:
Our method ::: Training Procedure ::: Fine-tune
In fine-tune stage, we jointly update the model on ASR, MT, and ST tasks  The training for ASR and MT follows the same process as it was in pre-training stage 
For ST task, the $enc_s$ reads the input $\mathbf {x}$ and generates $\mathbf {h^s}$, then $enc_t$ learns high-level linguistic knowledge into $\mathbf {h^t}$  Finally, the $dec$ predicts the target sentence  The ST loss function is defined as:
Following the update strategy proposed by BIBREF11 luong2015multi, we allocate a different training ratio $\alpha _i$ for each task  When switching between tasks, we select randomly a new task $i$ with probability $\frac{\alpha _i}{\sum _{j}\alpha _{j}}$ 
Our method ::: Subnet-Consistency
Our model keeps role consistency between pre-training and fine-tuning by connecting two encoders for ST task  However, this leads to some new problems: 1) The text encoder consumes $\mathbf {e^s}$ during MT training, while it accepts $\mathbf {h^s}$ during ST training  However, $\mathbf {e^s}$ and $\mathbf {h^s}$ may not follow the same distribution, resulting in the semantic inconsistency  2) Besides, the length of $\mathbf {h^s}$ is not the same order of magnitude with the length of $\mathbf {e^s}$, resulting in the length inconsistency 
In response to the above two challenges, we propose two countermeasures: 1) We share weights between CTC classification layer and source-end word embedding layer during training of ASR and MT, encouraging $\mathbf {e^s}$ and $\mathbf {h^s}$ in the same space  2)We feed the text encoder source sentences in the format of CTC path, which are generated from a seq2seq model, making it more robust toward long inputs 
Our method ::: Subnet-Consistency ::: Semantic Consistency
As shown in Figure FIGREF5, during multi-task training, two different hidden features will be fed into the text encoder $enc_t$: the embedding representation $\mathbf {e}^s$ in MT task, and the $enc_s$ output $\mathbf {h^s}$ in ST task  Without any regularization, they may belong to different latent spaces  Due to the space gap, the $enc_t$ has to compromise between two tasks, limiting its performance on individual tasks 
To bridge the space gap, our idea is to pull $\mathbf {h^s}$ into the latent space where $\mathbf {e}^s$ belong  Specifically, we share the weight $W_{ctc}$ in CTC classification layer with the source embedding weights $W_{E^s}$, which means $W_{ctc} = W_{E^s}$  In this way, when predicting the CTC path $\mathbf {\pi }$, the probability of observing the particular label $w_i \in V_{src}\cup ${`-'} at time step $t$, $p(\pi _t=w_i|\mathbf {x})$, is computed by normalizing the product of hidden vector $h_t^s$ and the $i$-th vector in $W_{E^s}$:
The loss function closes the distance between $h^s_t$ and golden embedding vector, encouraging $\mathbf {h}^s$ have the same distribution with $\mathbf {e}^s$ 
Our method ::: Subnet-Consistency ::: Length Consistency
Another existing problem is length inconsistency  The length of the sequence $\mathbf {h^s}$ is proportional to the length of the input frame $\mathbf {x}$, which is much longer than the length of $\mathbf {e^s}$  To solve this problem, we train an RNN-based seq2seq model to transform normal source sentences to noisy sentences in CTC path format, and replace standard MT with denoising MT for multi-tasking 
Specifically, we first train a CTC ASR model based on dataset $\mathcal {A} = \lbrace (\mathbf {x}_i, \mathbf {y}^s_i)\rbrace _{i=0}^{I}$, and generate a CTC-path $\mathbf {\pi }_i$ for each audio $\mathbf {x}_i$ by greedy decoding  Then we define an operation $S(\cdot )$, which converts a CTC path $\mathbf {\pi }$ to a sequence of the unique tokens $\mathbf {u}$ and a sequence of repetition times for each token $\mathbf {l}$, denoted as $S(\mathbf {\pi }) = (\mathbf {u}, \mathbf {l})$  Notably, the operation is reversible, meaning that $S^{-1} (\mathbf {u}, \mathbf {l})=\mathbf {\pi }$  We use the example $\mathbf {\pi _1}$ in Table TABREF14 and show the corresponding $\mathbf {u}$ and $\mathbf {l}$ in Table TABREF24 
Then we build a dataset $\mathcal {P} = \lbrace (\mathbf {y^s}_i, \mathbf {u}_i, \mathbf {l}_i)\rbrace _{i=0}^{I}$ by decoding all the audio pieces in $\mathcal {A}$ and transform the resulting path by the operation $S(\cdot )$  After that, we train a seq2seq model, as shown in Figure FIGREF25, which takes $ \mathbf {y^s}_i$ as input and decodes $\mathbf {u}_i, \mathbf {l}_i$ as outputs  With the seq2seq model, a noisy MT dataset $\mathcal {M}^{\prime }=\lbrace (\mathbf {\pi }_l, \mathbf {y^t}_l)\rbrace _{l=0}^{L}$ is obtained by converting every source sentence $\mathbf {y^s}_i \in \mathcal {M}$ to $\mathbf {\pi _i}$, where $\mathbf {\pi }_i = S^{-1}(\mathbf {u}_i, \mathbf {l}_i)$  We did not use the standard seq2seq model which takes $\mathbf {y^s}$ as input and generates $\mathbf {\pi }$ directly, since there are too many blank tokens `-' in $\mathbf {\pi }$ and the model tends to generate a long sequence with only blank tokens  During MT training, we randomly sample text pairs from $\mathcal {M}^{\prime }$ and $\mathcal {M}$ according to a hyper-parameter $k$  After tuning on the validation set, about $30\%$ pairs are sampled from $\mathcal {M}^{\prime }$  In this way, the $enc_t$ is more robust toward the longer inputs given by the $enc_s$ 
Experiments
We conduct experiments on the IWSLT18 speech translation task BIBREF1  Since IWSLT participators use different data pre-processing methods, we reproduce several competitive baselines based on the ESPnet BIBREF12 for a fair comparison 
Experiments ::: Dataset ::: Speech translation data:
The organizer provides a speech translation corpus extracting from the TED talk (ST-TED), which consists of raw English wave files, English transcriptions, and aligned German translations  The corpus contains 272 hours of English speech with 171k segments  We split 2k segments from the corpus as dev set and tst2010, tst2013, tst2014, tst2015 are used as test sets 
Speech recognition data: Aside from ST-TED, TED-LIUM2 corpus BIBREF13 is provided as speech recognition data, which contains 207 hours of English speech and 93k transcript sentences 
Text translation data: We use transcription and translation pairs in the ST-TED corpus and WIT3 as in-domain MT data, which contains 130k and 200k sentence pairs respectively  WMT2018 is used as out-of-domain training data which consists of 41M sentence pairs 
Data preprocessing: For speech data, the utterances are segmented into multiple frames with a 25 ms window size and a 10 ms step size  Then we extract 80-channel log-Mel filter bank and 3-dimensional pitch features using Kaldi BIBREF14, resulting in 83-dimensional input features  We normalize them by the mean and the standard deviation on the whole training set  The utterances with more than 3000 frames are discarded  The transcripts in ST-TED are in true-case with punctuation while in TED-LIUM2, transcripts are in lower-case and unpunctuated  Thus, we lowercase all the sentences and remove the punctuation to keep consistent  To increase the amount of training data, we perform speed perturbation on the raw signals with speed factors 0 9 and 1 1  For the text translation data, sentences longer than 80 words or shorter than 10 words are removed  Besides, we discard pairs whose length ratio between source and target sentence is smaller than 0 5 or larger than 2 0  Word tokenization is performed using the Moses scripts and both English and German words are in lower-case 
We use two different sets of vocabulary for our experiments  For the subword experiments, both English and German vocabularies are generated using sentencepiece BIBREF15 with a fixed size of 5k tokens  BIBREF9 inaguma2018speech show that increasing the vocabulary size is not helpful for ST task  For the character experiments, both English and German sentences are represented in the character level 
For evaluation, we segment each audio with the LIUM SpkDiarization tool BIBREF16 and then perform MWER segmentation with RWTH toolkit BIBREF17  We use lowercase BLEU as evaluation metric 
Experiments ::: Baseline Models and Implementation
We compare our method with following baselines 
Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder  It is trained from scratch on the ST-TED corpus 
Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained  The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus  The MT model has a text encoder and decoder with the same architecture of which in TCEN  It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data 
Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting  In the first two settings, we train the model with $\alpha _{st}=0 75$ while $\alpha _{asr}=0 25$ or $\alpha _{mt}=0 25$

ground_truth: improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3.5 F1 score, much larger than when a random set of expert annotations are added

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 baselines?
Context 3:  For many-to-many setting, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$   For MT task, we use only in-domain data 
Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models  Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation  Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model  Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder  For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model  The Triangle model is fine-tuned under their multi-task manner 
All our baselines as well as TCEN are implemented based on ESPnet BIBREF12, the RNN size is set as $d=1024$ for all models  We use a dropout of 0 3 for embeddings and encoders, and train using Adadelta with initial learning rate of 1 0 for a maximum of 10 epochs 
For training of TCEN, we set $\alpha _{asr}=0 2$ and $\alpha _{mt}=0 8$ in the pre-training stage, since the MT dataset is much larger than ASR dataset  For fine-tune, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$, same as the `many-to-many' baseline 
For testing, we select the model with the best accuracy on speech translation task on dev set  At inference time, we use a beam size of 10, and the beam scores include length normalization with a weight of 0 2 
Experiments ::: Experimental Results
Table TABREF29 shows the results on four test sets as well as the average performance  Our method significantly outperforms the strong `many-to-many+pretrain' baseline by 3 6 and 2 2 BLEU scores respectively, indicating the proposed method is very effective that substantially improves the translation quality  Besides, both pre-training and multi-task learning can improve translation quality, and the pre-training settings (2nd-4th rows) are more effective compared to multi-task settings (5th-8th rows)  We observe a performance degradation in the `triangle+pretrain' baseline  Compared to our method, where the decoder receives higher-level syntactic and semantic linguistic knowledge extracted from text encoder, their ASR decoder can only provide lower word-level linguistic information  Besides, since their model lacks text encoder and the architecture of ST decoder is different from MT decoder, their model cannot utilize the large-scale MT data in all the training stages  Interestingly, we find that the char-level models outperform the subword-level models in all settings, especially in vanilla baseline  A similar phenomenon is observed by BIBREF6 berard2018end  A possible explanation is that learning the alignments between speech frames and subword units in another language is notoriously difficult  Our method can bring more gains in the subword setting since our model is good at learning the text-to-text alignment and the subword-level alignment is more helpful to the translation quality 
Experiments ::: Discussion ::: Ablation Study
To better understand the contribution of each component, we perform an ablation study on subword-level experiments  The results are shown in Table TABREF37  In `-MT noise' setting, we do not add noise to source sentences for MT  In `-weight sharing' setting, we use different parameters in CTC classification layer and source embedding layer  These two experiments prove that both weight sharing and using noisy MT input benefit to the final translation quality  Performance degrades more in `-weight sharing', indicating the semantic consistency contributes more to our model  In the `-pretrain' experiment, we remove the pre-training stage and directly update the model on three tasks, leading to a dramatic decrease on BLEU score, indicating the pre-training is an indispensable step for end-to-end ST 
Experiments ::: Discussion ::: Learning Curve
It is interesting to investigate why our method is superior to baselines  We find that TCEN achieves a higher final result owing to a better start-point in fine-tuning  Figure FIGREF39 provides learning curves of subword accuracy on validation set  The x-axis denotes the fine-tuning training steps  The vanilla model starts at a low accuracy, because its networks are not pre-trained on the ASR and MT data  The trends of our model and `many-to-many+pretrain' are similar, but our model outperforms it about five points in the whole fine-tuning process  It indicates that the gain comes from bridging the gap between pre-training and fine-tuning rather than a better fine-tuning process 
Experiments ::: Discussion ::: Compared with a Cascaded System
Table TABREF29 compares our model with end-to-end baselines  Here, we compare our model with cascaded systems  We build a cascaded system by combining the ASR model and MT model used in pre-training baseline  Word error rate (WER) of the ASR system and BLEU score of the MT system are reported in the supplementary material  In addition to a simple combination of the ASR and MT systems, we also re-segment the ASR outputs before feeding to the MT system, denoted as cascaded+re-seg  Specifically, we train a seq2seq model BIBREF19 on the MT dataset, where the source side is a no punctuation sentence and the target side is a natural sentence  After that, we use the seq2seq model to add sentence boundaries and punctuation on ASR outputs  Experimental results are shown in Table TABREF41  Our end-to-end model outperforms the simple cascaded model over 2 BLEU scores, and it achieves a comparable performance with the cascaded model combining with a sentence re-segment model 
Related Work
Early works conduct speech translation in a pipeline manner BIBREF2, BIBREF20, where the ASR output lattices are fed into an MT system to generate target sentences  HMM BIBREF21, DenseNet BIBREF22, TDNN BIBREF23 are commonly used ASR systems, while RNN with attention BIBREF19 and Transformer BIBREF10 are top choices for MT  To enhance the robustness of the NMT model towards ASR errors, BIBREF24 DBLP:conf/eacl/TsvetkovMD14 and BIBREF25 DBLP:conf/asru/ChenHHL17 propose to simulate the noise in training and inference 
To avoid error propagation and high latency issues, recent works propose translating the acoustic speech into text in target language without yielding the source transcription BIBREF4  Since ST data is scarce, pre-training BIBREF7, multi-task learning BIBREF4, BIBREF6, curriculum learning BIBREF26, attention-passing BIBREF27, and knowledge distillation BIBREF28, BIBREF29 strategies have been explored to utilize ASR data and MT data  Specifically, BIBREF5 DBLP:conf/interspeech/WeissCJWC17 show improvements of performance by training the ST model jointly with the ASR and the MT model  BIBREF6 berard2018end observe faster convergence and better results due to pre-training and multi-task learning on a larger dataset  BIBREF7 DBLP:conf/naacl/BansalKLLG19 show that pre-training a speech encoder on one language can improve ST quality on a different source language  All of them follow the traditional multi-task training strategies  BIBREF26 DBLP:journals/corr/abs-1802-06003 propose to use curriculum learning to improve ST performance on syntactically distant language pairs  To effectively leverage transcriptions in ST data, BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 augment the multi-task model where the target decoder receives information from the source decoder and they show improvements on low-resource speech translation  Their model just consumes ASR and ST data, in contrast, our work sufficiently utilizes the large-scale MT data to capture the rich semantic knowledge  BIBREF30 DBLP:conf/icassp/JiaJMWCCALW19 use pre-trained MT and text-to-speech (TTS) synthesis models to convert weakly supervised data into ST pairs and demonstrate that an end-to-end MT model can be trained using only synthesised data 
Conclusion
This paper has investigated the end-to-end method for ST  It has discussed why there is a huge gap between pre-training and fine-tuning in previous methods  To alleviate these issues, we have proposed a method, which is capable of reusing every sub-net and keeping the role of sub-net consistent between pre-training and fine-tuning  Empirical studies have demonstrated that our model significantly outperforms baselines 

ground_truth: Vanilla ST baseline, encoder pre-training, in which the ST encoder is initialized from an ASR model, decoder pre-training, in which the ST decoder is initialized from an MT model, encoder-decoder pre-training, where both the encoder and decoder are pre-trained, many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models, Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation

Vanilla ST baseline, Pre-training baselines, Multi-task baselines, Many-to-many+pre-training, Triangle+pre-train

Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder. It is trained from scratch on the ST-TED corpus.

Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained. The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus. The MT model has a text encoder and decoder with the same architecture of which in TCEN. It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data.

Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting. In the first two settings, we train the model with $\alpha _{st}=0.75$ while $\alpha _{asr}=0.25$ or $\alpha _{mt}=0.25$. For many-to-many setting, we use $\alpha _{st}=0.6, \alpha _{asr}=0.2$ and $\alpha _{mt}=0.2$.. For MT task, we use only in-domain data.

Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models.