Question answering (QA) is the task of automatically producing an answer to a question given a corresponding document. It not only provides humans with efficient access to vast amounts of information, but also acts as an important proxy task to assess machine literacy via reading comprehension. Thanks to the recent release of several large-scale machine comprehension/QA datasets BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , the field has undergone significant advancement, with an array of neural models rapidly approaching human parity on some of these benchmarks BIBREF5 , BIBREF6 , BIBREF7 . However, previous models do not treat QA as a task of natural language generation (NLG), but of pointing to an answer span within a document.Alongside QA, question generation has also gained increased popularity BIBREF8 , BIBREF9 . The task is to generate a natural-language question conditioned on an answer and the corresponding document. Among its many applications, question generation has been used to improve QA systems BIBREF10 , BIBREF11 , BIBREF12 . A recurring theme among previous studies is to augment existing labeled data with machine-generated questions; to our knowledge, the direct (though implicit) effect of asking questions on answering questions has not yet been explored.In this work, we propose a joint model that both asks and answers questions, and investigate how this joint-training setup affects the individual tasks. We hypothesize that question generation can help models achieve better QA performance. This is motivated partly by observations made in psychology that devising questions while reading can increase scores on comprehension tests BIBREF13 . Our joint model also serves as a novel framework for improving QA performance outside of the network-architectural engineering that characterizes most previous studies.Although the question answering and asking tasks appear symmetric, there are some key differences. First, answering the questions in most existing QA datasets is extractive — it requires selecting some span of text within the document — while question asking is comparatively abstractive — it requires generation of text that may not appear in the document. Furthermore, a (document, question) pair typically specifies a unique answer. Conversely, a typical (document, answer) pair may be associated with multiple questions, since a valid question can be formed from any information or relations which uniquely specify the given answer.To tackle the joint task, we construct an attention-based BIBREF14 sequence-to-sequence model BIBREF15 that takes a document as input and generates a question (answer) conditioned on an answer (question) as output. To address the mixed extractive/abstractive nature of the generative targets, we use the pointer-softmax mechanism BIBREF16 that learns to switch between copying words from the document and generating words from a prescribed vocabulary. Joint training is realized by alternating the input data between question-answering and question-generating examples for the same model. We demonstrate empirically that this model's QA performance on SQuAD, while not state of the art, improves by about 10% with joint training. A key novelty of our joint model is that it can generate (partially) abstractive answers.Joint-learning on multiple related tasks has been explored previously BIBREF17 , BIBREF18 . In machine translation, for instance, BIBREF18 demonstrated that translation quality clearly improves over models trained with a single language pair when the attention mechanism in a neural translation model is shared and jointly trained on multiple language pairs.In question answering, BIBREF19 proposed one of the first neural models for the SQuAD dataset. SQuAD defines an extractive QA task wherein answers consist of word spans in the corresponding document. BIBREF19 demonstrated that learning to point to answer boundaries is more effective than learning to point sequentially to the tokens making up an answer span. Many later studies adopted this boundary model and achieved near-human performance on the task BIBREF5 , BIBREF6 , BIBREF7 . However, the boundary-pointing mechanism is not suitable for more open-ended tasks, including abstractive QA BIBREF4 and question generation. While “forcing” the extractive boundary model onto abstractive datasets currently yields state-of-the-art results BIBREF5 , this is mainly because current generative models are poor and NLG evaluation is unsolved.Earlier work on question generation has resorted to either rule-based reordering methods BIBREF20 , BIBREF21 , BIBREF22 or slot-filling with question templates BIBREF23 , BIBREF24 , BIBREF25 . These techniques often involve pipelines of independent components that are difficult to tune for final performance measures. Partly to address this limitation, end-to-end-trainable neural models have recently been proposed for question generation in both vision BIBREF26 and language. For example, BIBREF8 used a sequence-to-sequence model with an attention mechanism derived from the encoder states. BIBREF9 proposed a similar architecture but in addition improved model performance through policy gradient techniques.Several neural models with a questioning component have been proposed for the purpose of improving QA models, an objective shared by this study. BIBREF12 devised a semi-supervised training framework that trained a QA model BIBREF27 on both labeled data and artificial data generated by a separate generative component. BIBREF10 used policy gradient with a QA reward to train a sequence-to-sequence paraphrase model to reformulate questions in an existing QA dataset BIBREF2 . The generated questions were then used to further train an existing QA model BIBREF7 . A key distinction of our model is that we harness the process of asking questions to benefit question answering, without training the model to answer the generated questions.Our proposed model adopts a sequence-to-sequence framework BIBREF15 with an attention mechanism BIBREF14 and a pointer-softmax decoder BIBREF16 . Specifically, the model takes a document (i.e., a word sequence) $D = (w^d_1,\dots ,w^d_{n_d})$ and a condition sequence $C = (w^c_1,\dots ,w^c_{n_c})$ as input, and outputs a target sequence $Y^{\lbrace q,a\rbrace } = (\hat{w}_1,\dots ,\hat{w}_{n_p})$ . The condition corresponds to the question word sequence in answer-generation mode (a-gen), and the answer word sequence in question-generation mode (q-gen). We also attach a binary variable to indicate whether a data-point is intended for a-gen or q-gen. Intuitively, this should help the model learn the two modalities more easily. Empirically, QA performance improves slightly with this addition.A word $w_i$ in an input sequence is first embedded with an embedding layer into vector ${\bf e}^w_i$ . Character-level information is captured with the final states ${\bf e}^{ch}_i$ of a bidirectional Long Short-Term Memory model BIBREF28 on the character sequences of $w_i$ . The final representation for a word token ${\bf e}_i=\langle {\bf e}^w_i,{\bf e}^{ch}_i\rangle $ concatenates the word- and character-level embeddings. These are subsequently encoded with another BiLSTM into annotation vectors ${\bf h}^d_i$ and ${\bf h}^c_j$ (for the document and the condition sequence, respectively).To better encode the condition, we also extract the encodings of the document words that appear in the condition sequence. This procedure is particularly helpful in q-gen mode, where the condition (answer) sequence is typically extractive. These extracted vectors are then fed into a condition aggregation BiLSTM to produce the extractive condition encoding ${\bf h}^e_k$ . We specifically take the final states of the condition encodings ${\bf h}^c_J$ and ${\bf h}^e_K$ . To account for the different extractive vs. abstractive nature of questions vs. answers, we use ${\bf h}^c_J$ in a-gen mode (for encoding questions) and ${\bf h}^e_K$ in q-gen mode (for encoding answers).The RNN-based decoder employs the pointer-softmax mechanism BIBREF16 . At each generation step, the decoder decides adaptively whether (a) to generate from a decoder vocabulary or (b) to point to a word in the source sequence (and copy over). Recurrence of the pointing decoder is implemented with two LSTM cells $c_1$ and $c_2$ : $${\bf s}_1^{(t)} & = & c_1({\bf y}^{(t-1)}, {\bf s}_2^{(t-1)})\\

{\bf s}_2^{(t)} & = & c_2({\bf v}^{(t)}, {\bf s}_1^{(t)}),$$   (Eq. 1) where ${\bf s}_1^{(t)}$ and ${\bf s}_2^{(t)}$ are the recurrent states, ${\bf y}^{(t-1)}$ is the embedding of decoder output from the previous time step, and ${\bf v}^{(t)}$ is the context vector (to be defined shortly in Equation ( 2 )).The pointing decoder computes a distribution $\alpha ^{(t)}$ over the document word positions (i.e., a document attention, BIBREF14 ). Each element is defined as: $
\alpha ^{(t)}_i = f({\bf h}^d_i, {\bf h}^c, {\bf h}^e, {\bf s_1}^{(t-1)}),
$ where $f$ is a two-layer MLP with tanh and softmax activation, respectively. The context vector ${\bf v}^{(t)}$ used in Equation () is the sum of the document encoding weighted by the document attention: $${\bf v}^{(t)}=\sum _{i=1}^n \alpha ^{(t)}_i{\bf h}^d_i.$$   (Eq. 2) The generative decoder, on the other hand, defines a distribution over a prescribed decoder vocabulary with a two-layer MLP $g$ : $${\bf o}^{(t)}=g({\bf y}^{(t-1)},{\bf s}_2^{(t)},{\bf v}^{(t)},{\bf h}^c,{\bf h}^e).$$   (Eq. 3) Finally, the switch scalar $s^{(t)}$ at each time step is computed by a three-layer MLP $h$ : $
s^{(t)}=h({\bf s}_2^{(t)},{\bf v}^{(t)},\alpha ^{(t)},{\bf o}^{(t)}),
$ The first two layers of $h$ use tanh activation and the final layer uses sigmoid activation, and highway connections are present between the first and the second layer. We also attach the entropy of the softmax distributions to the input of the final layer, postulating that the quantities should help guide the switching mechanism by indicating the confidence of pointing vs generating. The addition is empirically observed to improve model performance.The resulting switch is used to interpolate the pointing and the generative probabilities for predicting the next word: $
p(\hat{w}_t)\sim s^{(t)} \alpha ^{(t)} + (1-s^{(t)}){\bf o}^{(t)}.
$ The optimization objective for updating the model parameters $\theta $ is to maximize the negative log likelihood of the generated sequences with respect to the training data $\mathcal {D}$ : $
\mathcal {L}=-\sum _{x\in \mathcal {D}}\log p(\hat{w}_t|w_{<t},x;\theta ).
$ Here, $w_{<t}$ corresponds to the embeddings ${\bf y}^{(t-1)}$ in Equation ( 1 ) and ( 3 ). During training, gold targets are used to teacher-force the sequence generation for training, i.e., $w_{<t}=w^{\lbrace q,a\rbrace }_{<t}$ , while during inference, generation is conditioned on the previously generated words, i.e., $w_{<t}=\hat{w}_{<t}$ .For words with multiple occurrence, since their exact references in the document cannot be reiabled determined, we aggregate the probability of these words in the encoder and the pointing decoder (similar to BIBREF29 ). At test time, beam search is used to enhance fluency in the question-generation output. The decoder also keeps an explicit history of previously generated words to avoid repetition in the output.We conduct our experiments on the SQuAD corpus BIBREF1 , a machine comprehension dataset consisting of over 100k crowd-sourced question-answer pairs on 536 Wikipedia articles. Simple preprocessing is performed, including lower-casing all texts in the dataset and using NLTK BIBREF30 for word tokenization. The test split of SQuAD is hidden from the public. We therefore take 5,158 question-answer pairs (self-contained in 23 Wikipedia articles) from the training set as validation set, and use the official development data to report test results. Note that answers in this dataset are strictly extractive, and we therefore constrain the pointer-softmax module to point at all decoding steps in answer generation mode.We first establish two baselines without multi-task training. Specifically, model A-gen is trained only to generate an answer given a document and a question, i.e., as a conventional QA model. Analogously, model Q-gen is trained only to generate questions from documents and answers. Joint-training (in model JointQA) is realized by feeding answer-generation and question-generation data to the model in an alternating fashion between mini-batches.In addition, we compare answer-generation performance with the sequence model variant of the match-LSTM (mLSTM) model BIBREF19 . As mentioned earlier, in contrast to existing neural QA models that point to the start and end boundaries of extractive answers, this model predicts a sequence of document positions as the answer. This makes it most comparable to our QA setup. Note, however, that our model has the additional capacity to generate abstractively from the decoder vocabulary.We use F1 and Exact Match (EM, BIBREF1 ) against the gold answer sequences to evaluate answer generation, and BLEU BIBREF31 against the gold question sequences to evaluate question generation. However, existing studies have shown that the task of question generation often exhibits linguistic variance that is semantically admissible; this renders it inappropriate to judge a generated question solely by matching against a gold sequence BIBREF9 . We therefore opt to assess the quality of generated questions $Y^q$ with two pretrained neural models as well: we use a language model to compute the perplexity of $Y^q$ , and a QA model to answer $Y^q$ . We measure the F1 score of the answer produced by this QA model.We choose mLSTM as the pretrained QA model and train it on SQuAD with the same split as mentioned in Section "Dataset" . Performance on the test set (i.e., the official validation set of SQuAD) is 73.78 F1 and 62.7 EM. For the pretrained language model, we train a single-layer LSTM language model on the combination of the text8 corpus, the Quora Question Pairs corpus, and the gold questions from SQuAD. The latter two corpora were included to tailor to our purpose of assessing question fluency, and for this reason, we ignore the semantic equivalence labels in the Quora dataset. Validation perplexity is 67.2 for the pretrained language model.Evaluation results are provided in Table 1 . We see that A-gen performance improves significantly with the joint model: both F1 and EM increase by about 10 percentage points. Performance of q-gen worsens after joint training, but the decrease is relatively small. Furthermore, as pointed out by earlier studies, automatic metrics often do not correlate well with the generation quality assessed by humans BIBREF9 . We thus consider the overall outcome to be positive.Meanwhile, although our model does not perform as well as mLSTM on the QA task, it has the added capability of generating questions. mLSTM uses a more advanced encoder tailored to QA, while our model uses only a bidirectional LSTM for encoding. Our model uses a more advanced decoder based on the pointer-softmax that enables it to generate abstactively and extractively.For a finer grained analysis, we first categorize test set answers based on their entity types, then stratify the QA performance comparison between A-gen and JointQA. The categorization relies on Stanford CoreNLP BIBREF32 to generate constituency parses, POS tags, and NER tags for answer spans (see BIBREF1 for more details). As seen in Figure 1 , the joint model significantly outperforms the single model in all categories. Interestingly, the moving average of the performance gap (dashed curve above bars) exhibits an upward trend as the A-gen model performance decreases across answer types, suggesting that the joint model helps most where the single model performance is weakest.Qualitatively, we have observed interesting “shifts” in attention before and after joint training. For example, in the positive case in Table 2 , the gold question asks about the direct object,Nixon, of the verb endorse, but the A-gen model predicts the indirect object, Kennedy, instead. In contrast, the joint model asks about the appositive of vice president during question generation, which presumably “primes” the model attention towards the correct answer Nixon. Analogously in the negative example, QA attention in the joint model appears to be shifted by joint training towards an answer that is incorrect but closer to the generated question.Note that the examples from Table 2 come from the validation set, and it is thus not possible for the joint model to memorize the gold answers from question-generation mode — the priming effect must come from some form of knowledge transfer between q-gen and a-gen via joint training.Implementation details of the proposed model are as follows. The encoder vocabulary indexes all words in the dataset. The decoder vocabulary uses the top 100 words sorted by their frequency in the gold questions in the training data. This encourages the model to generate frequent words (e.g. wh-words and function words) from the decoder vocabulary and copy less frequent ones (e.g., topical words and entities) from the document.The word embedding matrix is initialized with the 300-dimensional GloVe vectors BIBREF33 . The dimensionality of the character representations is 32. The number of hidden units is 384 for both of the encoder/decoder RNN cells. Dropout is applied at a rate of 0.3 to all embedding layers as well as between the hidden states in the encoder/decoder RNNs across time steps.We use adam BIBREF34 as the step rule for optimization with mini-batch size 32. The initial learning rate is $2e-4$ , which is decayed at a rate of 0.5 when the validation loss increases for two consecutive epochs.The model is implemented using Keras BIBREF35 with the Theano BIBREF36 backend.We proposed a neural machine comprehension model that can jointly ask and answer questions given a document. We hypothesized that question answering can benefit from synergistic interaction between the two tasks through parameter sharing and joint training under this multitask setting. Our proposed model adopts an attention-based sequence-to-sequence architecture that learns to dynamically switch between copying words from the document and generating words from a vocabulary. Experiments with the model confirm our hypothesis: the joint model outperforms its QA-only counterpart by a significant margin on the SQuAD dataset.Although evaluation scores are still lower than the state-of-the-art results achieved by dedicated QA models, the proposed model nonetheless demonstrates the effectiveness of joint training between QA and question generation, and thus offers a novel perspective and a promising direction for advancing the study of QA.

Q: Which components of QA and QG models are shared during training?

Q: How much improvement does jointly learning QA and QG give, compared to only training QA?