Natural language based consumer products, such as Apple Siri and Amazon Alexa, have found wide spread use in the last few years. A key requirement for these conversational systems is the ability to answer factual questions from the users, such as those about movies, music, and artists.Most of the current approaches for Question Answering (QA) are based on structured Knowledge Bases (KB) such as Freebase BIBREF0 and Wikidata BIBREF1 . In this setting the question is converted to a logical form using semantic parsing, which is queried against the KB to obtain the answer BIBREF2 , BIBREF3 . However, recent studies have shown that even large curated KBs, such as Freebase, are incomplete BIBREF4 . Further, KBs support only certain types of answer schemas, and constructing and maintaining them is expensive.On the other hand, there is a vast amount of unstructured knowledge available in textual form from web pages such as Wikipedia, and hence an alternative is to directly answer questions from these documents. In this approach, shown in Figure 1 , articles relevant to the question are first selected (retrieval step). Then, the retrieved articles and question are jointly processed to extract the answer (comprehension step). This retrieval based approach has a longer history than the KB based approach BIBREF5 . It can potentially provide a much wider coverage over questions, and is not limited to specific answer schemas. However, there are still gaps in its performance compared to the KB-based approach BIBREF6 . The comprehension step, which requires parsing information from natural language, is the main bottleneck, though suboptimal retrieval can also lead to lower performance.Several large-scale datasets introduced recently BIBREF7 , BIBREF8 have facilitated the development of powerful neural models for reading comprehension. These models fall into one of two categories: (1) those which extract answers as a span of text from the document BIBREF9 , BIBREF10 , BIBREF11 (Figure 2 top); (2) those which select the answer from a fixed vocabulary BIBREF12 , BIBREF6 (Figure 2 bottom). Here we argue that depending on the type of question, either (1) or (2) may be more appropriate, and introduce a latent variable mixture model to combine the two in a single end-to-end framework.We incorporate the above mixture model in a simple Recurrent Neural Network (RNN) architecture with an attention mechanism BIBREF13 for comprehension. In the second part of the paper we focus on the retrieval step for the QA system, and introduce a neural network based ranking model to select the articles to feed the comprehension model. We evaluate our model on WikiMovies dataset, which consists of 200K questions about movies, along with 18K Wikipedia articles for extracting the answers. KV:16 applied Key-Value Memory Neural Networks (KV-MemNN) to the dataset, achieving 76.2% accuracy. Adding the mixture model for answer selection improves the performance to 85.4%. Further, the ranking model improves both precision and recall of the retrieved articles, and leads to an overall performance of 85.8%.We focus on the WikiMovies dataset, proposed by BIBREF6 . The dataset consists of pairs of questions and answers about movies. Some examples are shown in Table 1 .As a knowledge source approximately 18K articles from Wikipedia are also provided, where each article is about a movie. Since movie articles can be very long, we only use the first paragraph of the article, which typically provides a summary of the movie. Formally, the dataset consists of question-answer pairs $\lbrace (q_j, A_j)\rbrace _{j=1}^J$ and movie articles $\lbrace d_k\rbrace _{k=1}^K$ . Additionally, the dataset includes a list of entities: movie titles, actor names, genres etc. Answers to all the questions are in the entity list. The questions are created by human annotators using SimpleQuestions BIBREF14 , an existing open-domain question answering dataset, and the annotated answers come from facts in two structured KBs: OMDb and MovieLens.There are two splits of the dataset. The “Full” dataset consists of 200K pairs of questions and answers. In this dataset, some questions are difficult to answer from Wikipedia articles alone. A second version of the dataset, “Wiki Entity” is constructed by removing those QA pairs where the entities in QAs are not found in corresponding Wikipedia articles. We call these splits WikiMovies-FL and WikiMovies-WE, respectively. The questions are divided into train, dev and test such that the same question template does not appear in different splits. Further, they can be categorized into 13 categories, including movie_to_actors, director_to_movies, etc. The basic statistics of the dataset are summarized in Table 2 .We also note that more than 50% of the entities appear less than 5 times in the training set. This makes it very difficult to learn the global statistics of each entity, necessitating the need to use an external knowledge source.Our QA system answers questions in two steps, as shown in Figure 1 . The first step is retrieval, where articles relevant to the question are retrieved. The second step is comprehension, where the question and retrieved articles are processed to derive answers.In this section we focus on the comprehension model, assuming that relevant articles have already been retrieved and merged into a context document. In the next section, we will discuss approaches for retrieving the articles. BIBREF6 , who introduced WikiMovies dataset, used an improved variant of Memory Networks called Key-Value Memory Networks. Instead, we use RNN based network, which has been successfully used in many reading comprehension tasks BIBREF10 , BIBREF9 , BIBREF12 .WikiMovies dataset has two notable differences from many of the existing comprehension datasets, such as CNN and SQuAD BIBREF10 , BIBREF9 , BIBREF12 . First, with imperfect retrieval, the answer may not be present in the context. We handle this case by using the proposed mixture model. Second, there may be multiple answers to a question, such as a list of actors. We handle this by optimizing a sum of the cross-entropy loss over all possible answers.We also use attention sum architecture proposed by BIBREF10 , which has been shown to give high performance for comprehension tasks. In this approach, attention scores over the context entities are used as the output. We term this the attention distribution $p_{att}$ , defined over the entities in the context. The mixture model combines this distribution with another output probability distribution $p_{vocab}$ over all the entities in the vocabulary. The intuition behind this is that named entities (such as actors and directors) can be better handled by the attention part, since there are few global statistics available for these, and other entities (such as languages and genres) can be captured by vocabulary part, for which global statistics can be leveraged.Let $\mathcal {V}$ be the vocabulary consisting of all tokens in the corpus, and $\mathcal {E}$ be the set of entities in the corpus The question is converted to a sequence of lower cased word ids, $(w_i) \in \mathcal {V}$ and a sequence of 0-1 flags for word capitalization, $(c_i) \in \lbrace 0,1\rbrace $ . For each word position $i$ , we also associate an entity id if the i-th word is part of an entity, $e_i \in \mathcal {E}$ (see Figure 3 ). Then, the combined embedding of the i-th position is given by $$x_i = W_w(w_i) + W_c(c_i) \Vert W_e(e_i), \hspace{7.22743pt} (i=1,\ldots ,L_q), $$   (Eq. 12) where $\Vert $ is the concatenation of two vectors, $L_q$ is the number of words in a question $q$ , and $W_w, W_c$ and $W_e$ are embedding matrices. Note that if there are no entities at i-th position, $W_e(e_i)$ is set to zero. The context is composed of up to $M$ movie articles concatenated with a special separation symbol. The contexts are embedded in exactly the same way as questions, sharing the embedding matrices.To avoid overfitting, we use another technique called anonymization. We limit the number of columns of $W_e$ to a relatively small number, $n_e$ , and entity ids are mapped to one of $n_e$ columns randomly (without collision). The map is common for each question/context pair but randomized across pairs. The method is similar to the anonymization method used in CNN / Daily Mail datasets BIBREF8 . emergent:16 showed that such a procedure actually helps readers since it adds coreference information to the system.Next, the question embedding sequence $(x_i)$ is fed into a bidirectional GRU (BiGRU) BIBREF15 to obtain a fixed length vector $v$ $$v = \overrightarrow{h}_{q}(L_q) \Vert \overleftarrow{h}_{q}(0), $$   (Eq. 13) where $\overrightarrow{h}_{q}$ and $\overleftarrow{h}_{q}$ are the final hidden states of forward and backward GRUs respectively.The context embedding sequence is fed into another BiGRU, to produce the output $H_c = [h_{c,1}, h_{c,2}, \ldots h_{c,L_c}]$ , where $L_c$ is the length of the context. An attention score for each word position $i$ is given by $$s_i \propto \exp ( v^T h_{c,i} ).$$   (Eq. 14) The probability over the entities in the context is then given by $$p_{att}(e) \propto \sum _{i \in I(e, c)} s_i,$$   (Eq. 15) where $I(e,c)$ is the set of word positions in the entity $e$ within the context $c$ .We next define the probability $p_{vocab}$ to be the probability over the complete set of entities in the corpus, given by $$p_{vocab}(e) = {\rm Softmax}(V u), $$   (Eq. 16) where the vector $u$ is given by $u = \sum _{i} s_i h_{c, i}$ . Each row of the matrix $V$ is the coefficient vector for an entity in the vocabulary. It is computed similar to Eq. ( 12 ). $$V(e) = \sum _{w \in e} W_w(w) + \sum _{c \in e} W_c(c) \Vert W_e(e). $$   (Eq. 17) The embedding matrices are shared between question and context.The final probability that an entity $e$ answers the question is given by the mixture $p(e) = (1-g) p_{att}(e) + g p_{vocab}(e)$ , with the mixture coefficient $g$ defined as $$g = \sigma (W_g g_0), \hspace{7.22743pt} g_0 = v^T u \Vert \max V u.$$   (Eq. 18) The two components of $g_0$ correspond to the attention part and vocabulary part respectively. Depending on the strength of each, the value of $g$ may be high or low.Since there may be multiple answers for a question, we optimize the sum of the probabilities: $$\textrm {loss} = - \log \Big ( \sum _{a \in A_j} p(a|q_j,c_j) \Big ) $$   (Eq. 19) Our overall model is displayed in Figure 4 .We note that KV-MemNN BIBREF6 employs “Title encoding” technique, which uses the prior knowledge that movie titles are often in answers. BIBREF6 showed that this technique substantially improves model performance by over 7% for WikiMovies-WE dataset. In our work, on the other hand, we do not use any data specific feature engineering.Our QA system answers questions by two steps as in Figure 1 . Accurate retrieval of relevant articles is essential for good performance of the comprehension model, and in this section we discuss three approaches for it. We use up to $M$ articles as context. A baseline approach for retrieval is to select articles which contain at least one entity also present in the question. We identify maximal intervals of words that match entities in questions and articles. Capitalization of words is ignored in this step because some words in the questions are not properly capitalized. Out of these (say $N$ ) articles we can randomly select $M$ . We call this approach (r0). For some movie titles, however, this method retrieves too many articles that are actually not related to questions. For example, there is a movie titled “Love Story” which accidentally picks up the words “love story”. This degrades the performance of the comprehension step. Hence, we describe two more retrieval models – (1) a dataset specific hand-crafted approach, and (2) a general learning based approach.In this approach, the $N$ articles retrieved using entity matching are assigned scores based on certain heuristics. If the movie title matches an entity in the question, the article is given a high score, since it is very likely to be relevant. A similar heuristic was also employed in BIBREF6 . In addition, the number of matching entities is also used to score each article. The top $M$ articles based on these scores are selected for comprehension. This hand-crafted approach already gives strong performance for the WikiMovies dataset, however the heuristic for matching article titles may not be appropriate for other QA tasks. Hence we also study a general learning based approach for retrieval.The learning model for retrieval is trained by an oracle constructed using distant supervision. Using the answer labels in the training set, we can find appropriate articles that include the information requested in the question. For example, for x_to_movie question type, the answer movie articles are the correct articles to be retrieved. On the other hand, for questions in movie_to_x type, the movie in the question should be retrieved. Having collected the labels, we train a retrieval model for classifying a question and article pair as relevant or not relevant.Figure 5 gives an overview of the model, which uses a Word Level Attention (WLA) mechanism. First, the question and article are embedded into vector sequences, using the same method as the comprehension model. We do not use anonymization here, to retain simplicity. Otherwise, the anonymization procedure would have to be repeated several times for a potentially large collection of documents. These vector sequences are next fed to a Bi-GRU, to produce the outputs $v$ (for the question) and $H_c$ (for the document) similar to the previous section.To classify the article as relevant or not, we introduce a novel attention mechanism to compute the score, $$s = \sum _{i} ((w \tilde{v} + b)^T \tilde{h}_{c,i})^4$$   (Eq. 25) Each term in the sum above corresponds to the match between the query representation and a token in the context. This is passed through a 4-th order non-linearity so that relevant tokens are emphasized more. Next, we compute the probability that the article is relevant using a sigmoid: $$o = \sigma (w^{\prime } s + b^{\prime })$$   (Eq. 27) In the above, $\tilde{x}$ is the normalized version (by L2-norm) of vector $x$ , $w, b, w^{\prime }, b^{\prime }$ are scalar learnable parameters to control scales.We evaluate the comprehension model on both WikiMovies-FL and WikiMovies-WE datasets. The performance is evaluated using the accuracy of the top hit (single answer) over all possible answers (all entities). This is called hits@1 metric.For the comprehension model, we use embedding dimension 100, and GRU dimension 128. We use up to $M=10$ retrieved articles as context. The order of the articles are randomly shuffled for each training instance to prevent over-fitting. The size of the anonymized entity set $n_e$ is 600, since in most of the cases, number of entities in a question and context pair is less than 600.For training the comprehension model, the Adam BIBREF16 optimization rule is used with batch size 32. We stop the optimization based on dev-set performance, and training takes around 10 epochs. For WikiMovies-FL (resp. WikiMovies-WE) dataset, each epoch took approximately 4 (resp. 2) hours on an Nvidia GTX1080 GPU.For training the retrieval model R2, we use a binary cross entropy objective. Since most articles are not relevant to a question, the ration of positive and negative samples is tuned to $1:10$ . Each epoch for training the retrieval model takes about 40 minutes on an Nvidia GTX1080 GPU.We evaluate the retrieval models based on precision and recall of the oracle articles. The evaluation is done on the test set. R@k is the ratio of cases where the highest ranked oracle article is in the top k retrieved articles. P@k is the ratio of oracle articles which are in the top k retrieved results. These numbers are summarized in Table 3 . We can see that both (r1) and (R2) significantly outperform (r0), with (R2) doing slightly better. We emphasize that (R2) uses no domain specific knowledge, and can be readily applied to other datasets where articles may not be about specific types of entities.We have also tested simpler models based on inner product of question and article vectors. In these models, a question $q_j$ and article $d_k$ are converted to vectors $\Phi (q_j), \Psi (d_k)$ , and the relevance score is given by their inner product: $${\rm score}(j,k) = \Phi (q_j)^T \Psi (d_k).$$   (Eq. 32) In the view of computation, those models are attractive because we can compute the article vectors offline, and do not need to compute the attention over words in the article. Maximum Inner Product Search algorithms may also be utilized here BIBREF17 , BIBREF18 . However, as shown in upper block of Table 4 , those models perform much worse in terms of scoring. The “Sum of Hidden State” and “Query Free Attention” models are similar to WLA model, using BiGRUs for question and article. In both of those models, $\Phi (q)$ is defined the same way as WLA model, Eq ( 13 ). For the “Sum of Hidden States” model, $\Psi (d)$ is given by the sum of BiGRU hidden states. This is the same as the proposed model by replacing the fourth order of WLA to one. For the “Query Free Attention” model, $\Psi (d)$ is given by the sum of BiGRU hidden states.We compare our model and several ablations with the KV-MemNN model. Table 5 shows the average performance across three evaluations. The (V) “Vocabulary Model” and (A) “Attention Model” are simplified versions of the full (AV) “Attention and Vocabulary Model”, using only $p_{vocab}$ and $p_{att}$ , respectively. Using a mixture of $p_{att}$ and $p_{vocab}$ gives the best performance.Interestingly, for WE dataset the Attention model works better. For FL dataset, on the other hand, it is often impossible to select answer from the context, and hence the Vocab model works better.The number of entities in the full vocabulary is 71K, and some of these are rare. Our intuition to use the Vocab model was to only use it for common entities, and hence we next constructed a smaller vocabulary consisting of all entities which appear at least 10 times in the corpus. This results in a subset vocabulary $\mathcal {V}_S$ of 2400 entities. Using this vocabulary in the mixture model (AsV) further improves the performance.Table 5 also shows a comparison between (r0), (r1), and (R2) in terms of the overall task performance. We can see that improving the quality of retrieved articles benefits the downstream comprehension performance. In line with the results of the previous section, (r1) and (R2) significantly outperform (r0). Among (r1) and (R2), (R2) performs slightly better.Table 6 shows the impact of anonymization of entities and shuffling of training articles before the comprehension step, described in Section "Comprehension Model" .Shuffling the context article before concatenating them, works as a data augmentation technique. Entity anonymization helps because without it each entity has one embedding. Since most of the entities appear only a few times in the articles, these embeddings may not be properly trained. Instead, the anonymous embedding vectors are trained to distinguish different entities. This technique is motivated by a similar procedure used in the construction of CNN / Daily Mail BIBREF8 , and discussed in detail in BIBREF19 .Figure 6 shows a test example from the WikiMovies-FL test data. In this case, even though the answers “Hindi” and “English” are not in the context, they are correctly estimated from $p_{vocab}$ . Note the high value of $g$ in this case. Figure 7 shows another example of how the mixture model works. Here the the answer is successfully selected from the document instead of the vocabulary. Note the low value of $g$ in this case.Table 7 shows the comparison for each category of questions between our model and KV-MemNN for the WikiMovies-WE dataset . We can see that performance improvements in the movie_to_x category is relatively large. The KV-MemNN model has a dataset specific “Title encoding” feature which helps the model x_to_movie question types. However without this feature performance in other categories is poor.The benefit of the mixture model comes from the fact that $p_{pointer}$ works well for some question types, while $p_{vocab}$ works well for others. Table 8 shows how often for each category $p_{vocab}$ is used ( $g > 0.5$ ) in AsV model. For question types “Movie to Language” and “Movie to Genre” (the so called “choice questions”) the number of possible answers is small. For this case, even if the answer can be found in the context, it is easier for the model to select answer from an external vocabulary which encodes global statistics about the entities. For other “free questions”, depending on the question type, one approach is better than the other. Our model is able to successfully estimate the latent category and switch the model type by controlling the coefficient $g$ .hierarchical:16 solve the QA problem by selecting a sentence in the document. They show that joint training of selection and comprehension slightly improves the performance. In our case, joint training is much harder because of the large number of movie articles. Hence we introduce a two-step retrieval and comprehension approach.Recently architecture:16 proposed a framework to use the performance on a downstream task (e.g. comprehension) as a signal to guide the learning of neural network which determines the input to the downstream task (e.g. retrieval). This motivates us to introduce neural network based approach for both retrieval and comprehension, since in this case the retrieval step can be directly trained to maximize the downstream performance.In the context of language modeling, the idea of combining of two output probabilities is given in BIBREF20 , however, our equation to compute the mixture coefficient is slightly different. More recently, ahn2016neural used a mixture model to predict the next word from either the entire vocabulary, or a set of Knowledge Base facts associated with the text. In this work, we present the first application of such a mixture model to reading comprehension.We have developed QA system using a two-step retrieval and comprehension approach. The comprehension step uses a mixture model to achieve state of the art performance on WikiMovies dataset, improving previous work by a significant margin.We would like to emphasize that our approach has minimal heuristics and does not use dataset specific feature engineering. Efficient retrieval while maintaining representation variation is a challenging problem. While there has been a lot of research on comprehension, little focus has been given to designing neural network based retrieval models. We present a simple such model, and emphasize the importance of this direction of research.

Q: How can a neural model be used for a retrieval if the input is the entire Wikipedia?