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.

Introduction
Deep learning systems have shown a lot of promise for extractive Question Answering (QA), with performance comparable to humans when large scale data is available. However, practitioners looking to build QA systems for specific applications may not have the resources to collect tens of thousands of questions on corpora of their choice. At the same time, state-of-the-art machine reading systems do not lend well to low-resource QA settings where the number of labeled question-answer pairs are limited (c.f. Table 2 ). Semi-supervised QA methods like BIBREF0 aim to improve this performance by leveraging unlabeled data which is easier to collect.
In this work, we present a semi-supervised QA system which requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents. Our proposed system consists of three stages. First, we construct cloze-style questions (predicting missing spans of text) from the unlabeled corpus; next, we use the generated clozes to pre-train a powerful neural network model for extractive QA BIBREF1 , BIBREF2 ; and finally, we fine-tune the model on the small set of provided QA pairs.
Our cloze construction process builds on a typical writing phenomenon and document structure: an introduction precedes and summarizes the main body of the article. Many large corpora follow such a structure, including Wikipedia, academic papers, and news articles. We hypothesize that we can benefit from the un-annotated corpora to better answer various questions – at least ones that are lexically similar to the content in base documents and directly require factual information.
We apply the proposed system on three datasets from different domains – SQuAD BIBREF3 , TriviaQA-Web BIBREF4 and the BioASQ challenge BIBREF5 . We observe significant improvements in a low-resource setting across all three datasets. For SQuAD and TriviaQA, we attain an F1 score of more than 50% by merely using 1% of the training data. Our system outperforms the approaches for semi-supervised QA presented in BIBREF0 , and a baseline which uses the same unlabeled data but with a language modeling objective for pretraining. In the BioASQ challenge, we outperform the best performing system from previous year's challenge, improving over a baseline which does transfer learning from the SQuAD dataset. Our analysis reveals that questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes.
Related Work
Semi-supervised learning augments the labeled dataset $L$ with a potentially larger unlabeled dataset $U$ . BIBREF0 presented a model, GDAN, which trained an auxiliary neural network to generate questions from passages by reinforcement learning, and augment the labeled dataset with the generated questions to train the QA model. Here we use a much simpler heuristic to generate the auxiliary questions, which also turns out to be more effective as we show superior performance compared to GDAN. Several approaches have been suggested for generating natural questions BIBREF6 , BIBREF7 , BIBREF8 , however none of them show a significant improvement of using the generated questions in a semi-supervised setting. Recent papers also use unlabeled data for QA by training large language models and extracting contextual word vectors from them to input to the QA model BIBREF9 , BIBREF10 , BIBREF11 . The applicability of this method in the low-resource setting is unclear as the extra inputs increase the number of parameters in the QA model, however, our pretraining can be easily applied to these models as well.
Domain adaptation (and Transfer learning) leverage existing large scale datasets from a source domain (or task) to improve performance on a target domain (or task). For deep learning and QA, a common approach is to pretrain on the source dataset and then fine-tune on the target dataset BIBREF12 , BIBREF13 . BIBREF14 used SQuAD as a source for the target BioASQ dataset, and BIBREF15 used Book Test BIBREF16 as source for the target SQuAD dataset. BIBREF17 transfer learned model layers from the tasks of sequence labeling, text classification and relation classification to show small improvements on SQuAD. All these works use manually curated source datatset, which in themselves are expensive to collect. Instead, we show that it is possible to automatically construct the source dataset from the same domain as the target, which turns out to be more beneficial in terms of performance as well (c.f. Section "Experiments & Results" ). Several cloze datasets have been proposed in the literature which use heuristics for construction BIBREF18 , BIBREF19 , BIBREF20 . We further see the usability of such a dataset in a semi-supervised setting.
Methodology
Our system comprises of following three steps:
Cloze generation: Most of the documents typically follow a template, they begin with an introduction that provides an overview and a brief summary for what is to follow. We assume such a structure while constructing our cloze style questions. When there is no clear demarcation, we treat the first $K\%$ (hyperparameter, in our case 20%) of the document as the introduction. While noisy, this heuristic generates a large number of clozes given any corpus, which we found to be beneficial for semi-supervised learning despite the noise.
We use a standard NLP pipeline based on Stanford CoreNLP (for SQuAD, TrivaQA and PubMed) and the BANNER Named Entity Recognizer (only for PubMed articles) to identify entities and phrases. Assume that a document comprises of introduction sentences $\lbrace q_1, q_2, ... q_n\rbrace $ , and the remaining passages $\lbrace p_1, p_2, .. p_m\rbrace $ . Additionally, let's say that each sentence $q_i$ in introduction is composed of words $\lbrace w_1, w_2, ... w_{l_{q_i}}\rbrace $ , where $l_{q_i}$ is the length of $q_i$ . We consider a $\text{match} (q_i, p_j)$ , if there is an exact string match of a sequence of words $\lbrace w_k, w_{k+1}, .. w_{l_{q_i}}\rbrace $ between the sentence $q_i$ and passage $p_j$ . If this sequence is either a noun phrase, verb phrase, adjective phrase or a named entity in $\lbrace p_1, p_2, .. p_m\rbrace $0 , as recognized by CoreNLP or BANNER, we select it as an answer span $\lbrace p_1, p_2, .. p_m\rbrace $1 . Additionally, we use $\lbrace p_1, p_2, .. p_m\rbrace $2 as the passage $\lbrace p_1, p_2, .. p_m\rbrace $3 and form a cloze question $\lbrace p_1, p_2, .. p_m\rbrace $4 from the answer bearing sentence $\lbrace p_1, p_2, .. p_m\rbrace $5 by replacing $\lbrace p_1, p_2, .. p_m\rbrace $6 with a placeholder. As a result, we obtain passage-question-answer ( $\lbrace p_1, p_2, .. p_m\rbrace $7 ) triples (Table 1 shows an example). As a post-processing step, we prune out $\lbrace p_1, p_2, .. p_m\rbrace $8 triples where the word overlap between the question (Q) and passage (P) is less than 2 words (after excluding the stop words).
The process relies on the fact that answer candidates from the introduction are likely to be discussed in detail in the remainder of the article. In effect, the cloze question from the introduction and the matching paragraph in the body forms a question and context passage pair. We create two cloze datasets, one each from Wikipedia corpus (for SQuAD and TriviaQA) and PUBMed academic papers (for the BioASQ challenge), consisting of 2.2M and 1M clozes respectively. From analyzing the cloze data manually, we were able to answer 76% times for the Wikipedia set and 80% times for the PUBMed set using the information in the passage. In most cases the cloze paraphrased the information in the passage, which we hypothesized to be a useful signal for the downstream QA task.
We also investigate the utility of forming subsets of the large cloze corpus, where we select the top passage-question-answer triples, based on the different criteria, like i) jaccard similarity of answer bearing sentence in introduction and the passage ii) the tf-idf scores of answer candidates and iii) the length of answer candidates. However, we empirically find that we were better off using the entire set rather than these subsets.
Pre-training: We make use of the generated cloze dataset to pre-train an expressive neural network designed for the task of reading comprehension. We work with two publicly available neural network models – the GA Reader BIBREF2 (to enable comparison with prior work) and BiDAF + Self-Attention (SA) model from BIBREF1 (which is among the best performing models on SQuAD and TriviaQA). After pretraining, the performance of BiDAF+SA on a dev set of the (Wikipedia) cloze questions is 0.58 F1 score and 0.55 Exact Match (EM) score. This implies that the cloze corpus is neither too easy, nor too difficult to answer.
Fine Tuning: We fine tune the pre-trained model, from the previous step, over a small set of labelled question-answer pairs. As we shall later see, this step is crucial, and it only requires a handful of labelled questions to achieve a significant proportion of the performance typically attained by training on tens of thousands of questions.
Datasets
We apply our system to three datasets from different domains. SQuAD BIBREF3 consists of questions whose answers are free form spans of text from passages in Wikipedia articles. We follow the same setting as in BIBREF0 , and split $10\%$ of training questions as the test set, and report performance when training on subsets of the remaining data ranging from $1\%$ to $90\%$ of the full set. We also report the performance on the dev set when trained on the full training set ( $1^\ast $ in Table 2 ). We use the same hyperparameter settings as in prior work. We compare and study four different settings: 1) the Supervised Learning (SL) setting, which is only trained on the supervised data, 2) the best performing GDAN model from BIBREF0 , 3) pretraining on a Language Modeling (LM) objective and fine-tuning on the supervised data, and 4) pretraining on the Cloze dataset and fine-tuning on the supervised data. The LM and Cloze methods use exactly the same data for pretraining, but differ in the loss functions used. We report F1 and EM scores on our test set using the official evaluation scripts provided by the authors of the dataset.
TriviaQA BIBREF4 comprises of over 95K web question-answer-evidence triples. Like SQuAD, the answers are spans of text. Similar to the setting in SQuAD, we create multiple smaller subsets of the entire set. For our semi-supervised QA system, we use the BiDAF+SA model BIBREF1 – the highest performing publicly available system for TrivaQA. Here again, we compare the supervised learning (SL) settings against the pretraining on Cloze set and fine tuning on the supervised set. We report F1 and EM scores on the dev set.
We also test on the BioASQ 5b dataset, which consists of question-answer pairs from PubMed abstracts. We use the publicly available system from BIBREF14 , and follow the exact same setup as theirs, focusing only on factoid and list questions. For this setting, there are only 899 questions for training. Since this is already a low-resource problem we only report results using 5-fold cross-validation on all the available data. We report Mean Reciprocal Rank (MRR) on the factoid questions, and F1 score for the list questions.
Main Results
Table 2 shows a comparison of the discussed settings on both SQuAD and TriviaQA. Without any fine-tuning (column 0) the performance is low, probably because the model never saw a real question, but we see significant gains with Cloze pretraining even with very little labeled data. The BiDAF+SA model, exceeds an F1 score of $50\%$ with only $1\%$ of the training data (454 questions for SQuAD, and 746 questions for TriviaQA), and approaches $90\%$ of the best performance with only $10\%$ labeled data. The gains over the SL setting, however, diminish as the size of the labeled set increases and are small when the full dataset is available.
Cloze pretraining outperforms the GDAN baseline from BIBREF0 using the same SQuAD dataset splits. Additionally, we show improvements in the $90\%$ data case unlike GDAN. Our approach is also applicable in the extremely low-resource setting of $1\%$ data, which we suspect GDAN might have trouble with since it uses the labeled data to do reinforcement learning. Furthermore, we are able to use the same cloze dataset to improve performance on both SQuAD and TriviaQA datasets. When we use the same unlabeled data to pre-train with a language modeling objective, the performance is worse, showing the bias we introduce by constructing clozes is important.
On the BioASQ dataset (Table 3 ) we again see a significant improvement when pretraining with the cloze questions over the supervised baseline. The improvement is smaller than what we observe with SQuAD and TriviaQA datasets – we believe this is because questions are generally more difficult in BioASQ. BIBREF14 showed that pretraining on SQuAD dataset improves the downstream performance on BioASQ. Here, we show a much larger improvement by pretraining on cloze questions constructed in an unsupervised manner from the same domain.
Analysis
Regression Analysis: To understand which types of questions benefit from pre-training, we pre-specified certain features (see Figure 1 right) for each of the dev set questions in SQuAD, and then performed linear regression to predict the F1 score for that question from these features. We predict the F1 scores from the cloze pretrained model ( $y^{\text{cloze}}$ ), the supervised model ( $y^{\text{sl}}$ ), and the difference of the two ( $y^{\text{cloze}}-y^{\text{sl}}$ ), when using $10\%$ of labeled data. The coefficients of the fitted model are shown in Figure 1 (left) along with their std errors. Positive coefficients indicate that a high value of that feature is predictive of a high F1 score, and a negative coefficient indicates that a small value of that feature is predictive of a high F1 score (or a high difference of F1 scores from the two models in the case of $y^{\text{cloze}}-y^{\text{sl}}$ ).
The two strongest effects we observe are that a high lexical overlap between the question and the sentence containing the answer is indicative of high boost with pretraining, and that a high lexical overlap between the question and the whole passage is indicative of the opposite. This is hardly surprising, since our cloze construction process is biased towards questions which have a similar phrasing to the answer sentences in context. Hence, test questions with a similar property are answered correctly after pretraining, whereas those with a high overlap with the whole passage tend to have lower performance. The pretraining also favors questions with short answers because the cloze construction process produces short answer spans. Also passages and questions which consist of tokens infrequent in the SQuAD training corpus receive a large boost after pretraining, since the unlabeled data covers a larger domain.
Performance on question types: Figure 2 shows the average gain in F1 score for different types of questions, when we pretrain on the clozes compared to the supervised case. This analysis is done on the $10\%$ split of the SQuAD training set. We consider two classifications of each question – one determined on the first word (usually a wh-word) of the question (Figure 2 (bottom)) and one based on the output of a separate question type classifier adapted from BIBREF21 . We use the coarse grain labels namely Abbreviation (ABBR), Entity (ENTY), Description (DESC), Human (HUM), Location (LOC), Numeric (NUM) trained on a Logistic Regression classification system . While there is an improvement across the board, we find that abbreviation questions in particular receive a large boost. Also, "why" questions show the least improvement, which is in line with our expectation, since these usually require reasoning or world knowledge which cloze questions rarely require.
Conclusion
In this paper, we show that pre-training QA models with automatically constructed cloze questions improves the performance of the models significantly, especially when there are few labeled examples. The performance of the model trained only on the cloze questions is poor, validating the need for fine-tuning. Through regression analysis, we find that pretraining helps with questions which ask for factual information located in a specific part of the context. For future work, we plan to explore the active learning setup for this task – specifically, which passages and / or types of questions can we select to annotate, such that there is a maximum performance gain from fine-tuning. We also want to explore how to adapt cloze style pre-training to NLP tasks other than QA.
Acknowledgments
Bhuwan Dhingra is supported by NSF under grants CCF-1414030 and IIS-1250956 and by grants from Google. Danish Pruthi and Dheeraj Rajagopal are supported by the DARPA Big Mechanism program under ARO contract W911NF-14-1-0436.
Please answer the question based on the text provided above. Is it possible to convert a cloze-style questions to a naturally-looking questions?

Answer: Unanswerable Unanswerable