Title: EMERGENT PREDICATION STRUCTURE IN VECTOR REPRESENTATIONS OF NEURAL READERS

Abstract: Reading comprehension is a question answering task where the answer is to be found in a given passage about entities and events not mentioned in general knowledge sources. A significant number of neural architectures for this task (neural readers) have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the emergence of "predication structure" in the hidden state vectors of a class of neural readers including the Attentive Reader and Stanford Reader. We posits that the hidden state vectors can be viewed as (a representation of) a concatenation [P, c] of a "predicate vector" P and a "constant symbol vector" c and that the hidden state represents the atomic formula P (c). This predication structure plays a conceptual role in relating "aggregation readers" such as the Attentive Reader and the Stanford Reader to "explicit reference readers" such as the Attention-Sum Reader, the Gated-Attention Reader and the Attention-over-Attention Reader. In an independent contribution, we show that the addition of linguistics features to the input to existing neural readers significantly boosts performance yielding the best results to date on the Who-did-What dataset.

1. INTRODUCTION AND OVERVIEW:

Reading comprehension is a type of question answering task where the answer is to be found in a passage about particular entities and events not otherwise familiar to the reader. In particular, the entities and events should not be mentioned in structured databases of general knowledge. Reading comprehension problems are intended to measure a systems ability to extract semantic information about entities and relations directly from unstructured text. Several large scale reading comprehension datasets have been introduced recently. In particular the CNN & DailyMail datasets (Hermann et al., 2015) , the Children's Book Test (CBT) (Hill et al., 2016) , and the Who-did-What dataset (Onishi et al., 2016) . The large sizes of these datasets enable the application of deep learning. These are all cloze-style datasets where a question is constructed by deleting a word or phrase from an article summary (in CNN/DailyMail), from a sentence in a Children's story (in CBT), or by deleting a person from the first sentence of a different news article on the same entities and events (in Who-did-What).

In this paper we present empirical evidence for the emergence of predication structure in a certain class of neural readers. To understand predication structure is it helful to review the anonymization performed in the CNN/DailyMail dataset. In this dataset named entities are replaced by anonymous entity identifiers such as "entity37". The passage might contain "entity52 gave entity24 a rousing applause" and the question might be "X received a rounding applause from entity52". The task is to fill in X from a given multiple choice list of candidate entity identifiers. A fixed relatively small set of the same entity identifiers are used over all the problems and the same problem is presented many times with the entity identifiers shuffled. This prevents a given entity identifier from having any semantically meaningful vector embedding. The embeddings of the entity identifiers are presumably just pointers to semantics-free tokens. We will write entity identifiers as logical constant symbols such as c rather than strings such as "entity37".

Aggregation readers, including Memory Networks (Weston et al.; Sukhbaatar et al., 2015) , the Attentive Reader (Hermann et al., 2015) and the Stanford Reader (Chen et al., 2016) , use bidirectional LSTMs or GRUs to construct a contextual embedding h t of each position t in the passage and also an embedding q of the question. They then select and answer c using a criterion similar to argmax c t < h t , q > < h t , e(c) >

where e(c) is the vector embedding of the constant symbol (entity identifier) c. In practice the inner-product < h t , q > is normalized over t using a softmax to yield an attention α t over t and (1) becomes. argmax c < e(c),

EQUATION

Here t α t h t is viewed as a vector representation of the passage.

We argue that for aggregation readers, roughly defined by (2), the hidden state h t of the passage at position (or word) t can be viewed as a vector concatenation h t = [e(Φ t ), e (c t )] where Φ t is a property (or statement or predicate) being stated of a particular constant symbol c t . A logician might write this as h t = Φ t [c t ]. Furthermore, the question can be interpreted as having the form

Ψ[x]

where the problem is to find a constant symbol c such that the passage implies Ψ[c]. Assuming h t = [e(Φ t ), e (c t )] and q = [e(Ψ), 0] and e(c) = [0, e (c)] we can rewrite (1) as

argmax c t

< e(Φ t ), e(Ψ) > < e (c t ), e (c) > .

(3)

The first inner product in (3) is interpreted as measuring the extent to which Φ t [x] implies Ψ[x] for any x. The second inner product is interpreted as restricting t to positions talking about the constant symbol c.

Note that the posited decomposition of h t is not explicit in (2) but instead must emerge during training. We present empirical evidence that this structure does emerge. The empirical evidence is somewhat tricky as the direct sum structure that divides h t into its two parts need not be axis aligned and therefore need not literally correspond to vector concatenation.

We also consider a second class of neural readers that we call explicit reference readers. Explicit reference readers avoid (2) and instead use

EQUATION

where R(c) is the subset of the positions where the constant symbol (entity identifier) c occurs.

Note that if we identify α t with < e(Φ t ), e(Ψ) > and assume that < e (c), e (c t ) > is either 0 or 1 depending on whether c = c t , then (3) and (4) agree. In explicit reference readers the hidden state h t need not carry a pointer to c t as the restriction on t is independent of learned representations. Explicit reference readers include the Attention Sum Reader (Kadlec et al., 2016) , the Gated Attention Reader (Dhingra et al., 2016) , the Attention-over-Attention Reader (Cui et al., 2016) and others (a list can be found in section 6).

So far we have only considered anonymized datasets that require the handling of semantics-free constant symbols. However, even for non-anonymized datasets such as Who-Did-What, it is helpful to add features which indicate which positions in the passage are referring to which candidate answers. This indicates, not surprisingly, that reference is important in question answering. The fact that explicit reference features are needed in aggregation readers on non-anonymized data indicates that reference is not being solved by the aggregation readers. However, as reference seems to be important for cloze-style question answering, these problems may ultimately provide training data from which reference resolution can be learned.

Sections 2 and 3 review various existing datasets and models respectively. Section 4 presents the logical structure interpretation of aggregation readers in more detail and the empirical evidence supporting it. Section 5 proposes new models that enforce the direct sum structure of the hidden state vectors. It is shown that these new models perform well on the Who-did-What dataset provided that reference annotations are added as input features. Section 5 also describes additional linguistic features that can be added to the input embeddings and show that these improve the performance of existing models resulting in the best single-model performance to date on the Who-did-What dataset.

2. A BRIEF SURVEY OF DATASETS:

Before presenting various models for machine comprehension we give a general formulation of the machine comprehension task. We take an instance of the task be a four tuple (q, p, a, A), where q is a question given as sequence of words containing a special taken for a "blank" to be filled in, p is a document consisting of a sequence of words, A is a set of possible answers and a ∈ A is the ground truth answer. All words are drawn from a vocabulary V. We assume that all possible answers are words from the vocabulary, that is A ⊆ V, and that the ground truth answer appears in the document, that is a ∈ p. The problem can be described as that of selecting the answer a ∈ A that answers question q based on information from p.

We will now briefly summarize important features of the related datasets in reading comprehension.

CNN & DailyMail: Hermann et al. ( 2015) constructed these datasets from a large number of news articles from the CNN and Daily Mail news websites. The main article is used as the context, while the cloze style question is formed from one short highlight sentence appearing in conjunction with the published article. To avoid the model using external world knowledge when answering the question, the named entities in the entire dataset were replaced by anonymous entity IDs which were then further shuffled for each example. This forces models to rely on the context document to answer each question. In this anonymized corpus the entity identifiers are taken to be a part of the vocabulary and the answer set A consists of the entity identifiers occurring in the passage.

Who-did-What (WDW): The Who-did-What dataset (Onishi et al., 2016) contains 127,000 multiple choice cloze questions constructed from the LDC English Gigaword newswire corpus (David & Cieri, 2003) . In contrast with CNN and Daily Mail, it avoids using article summaries for question formation. Instead, each problem is formed from two independent articles: one is given as the passage to be read and a different article on the same entities and events is used to form the question. Further, Who-did-What avoids anonymization, as each choice is a person named entity. In this dataset the answer set A consists of the person named entities occurring in the passage. Finally, the problems have been filtered to remove a fraction that are easily solved by simple baselines. It has two training sets. The larger training set ("relaxed") is created using less baseline filtering, while the smaller training set ("strict") uses the same filtering as the validation and test sets.

Children's Book Test (CBT) Hill et al. (2016) developed the CBT dataset in a slightly different fashion to the CNN/DailyMail datasets. They take any sequence of 21 consecutive sentences from a children's book: the first 20 sentences are used as the passage, and the goal is to infer a missing word in the 21st sentence. The task complexity varies with the type of the omitted word (verb, preposition, named entity, or common noun). According to the original study on this dataset (Hill et al., 2016) , n-gram and recurrent neural network language models are sufficient for predicting verbs or prepositions. However, for named entities and common nouns, current solvers are still far from human performance.

Other Related Datasets. It is also worth mentioning several related datasets. The MCTest dataset (Richardson et al., 2013) consists of children's stories and questions written by crowdsourced workers. The dataset only contains 660 documents and is too small to train deep models. The bAbI dataset (Weston et al., 2016) is constructed automatically using synthetic text generation and can be perfectly answered by hand-written algorithms (Lee et al., 2016) . The SQuAD dataset (Rajpurkar et al., 2016) consists passage-question pairs where the passage is a wikipedia article and the questions are written by crowdsourced workers. Although crowdsourcing is involved, the dataset contains over 200,000 problems. But the answer is often a word sequence which is dificult to handle with the reader models considered here. The LAMBADA dataset (Denis et al., 2016 ) is a word prediction dataset which requires a broad discourse context and the correct answer might not in the context. Nonetheless, when the correct answer is in the context, neural readers can be applied effectively (Chu et al., 2016) .

3. AGGREGATION READERS AND EXPLICIT REFERENCE READERS:

Here we classify readers into aggregation readers and explicit reference readers. Aggregation readers appeared first in the literature and include Memory Networks (Weston et al.; Sukhbaatar et al., 2015) , the Attentive Reader (Hermann et al., 2015) , and the Stanford Reader (Chen et al., 2016) . Aggregation readers are defined by equations ( 8) and (10) below. Explicit reference readers incluce the Attention-Sum Reader (Kadlec et al., 2016) , the Gated-Attention Reader (Dhingra et al., 2016) , and the Attention-over-Attention Reader (Cui et al., 2016) . Explicit reference readers are defined by equation ( 14) below. We first present the Stanford Reader as a paradigmatic aggregation Reader and the Attention-Sum Reader as a paradigmatic explicit reference reader.

3.1. AGGREGATION READERS:

Stanford Reader. The the Stanford Reader (Chen et al., 2016) computes a bi-directional LSTM representation of both the passage and the question.

EQUATION

In equations ( 5) and ( 6) we have that e(p) is the sequence of word embeddings e(w i ) for w i ∈ p and similarly for e(q). The expression biLSTM(s) denotes the sequence of hidden state vectors resulting from running a bi-directional LSTM on the vector sequence s. We write biLSTM(s) i for the ith vector in this sequence. Similarly fLSTM(s) and bLSTM(s) denote the sequence of vectors resulting from running a forward LSTM and a backward LSTM respectively and [•, •] denotes vector concatenation. The Stanford Reader, and various other readers, then compute a bilinear attention over the passage which is then used to construct a single weighted vector representation of the passage.

α t = softmax t h t W α q (7) o = t α t h t (8)

Finally, they compute a probability distribution over the answers P (a|p, q, A).

EQUATION

Here e o (a) is an "output embedding" of the answer a. On the CNN dataset the Stanford Reader trains an output embedding for each the roughly 500 entity identifiers used in the dataset. In cases where the answer might be any word in V an output embedding must be trained for the entire vocabulary.

The reader is trained with log-loss ln 1/P (a|p, q, A) where a is the correct answer. At test time the reader is scored on the percentage of problems where â = a.

Memory Networks. Memory Networks (Weston et al.; Sukhbaatar et al., 2015) use ( 8) and ( 10) but have more elaborate methods of constructing "memory vectors" h t not involve LSTMs. Memory networks use ( 8) and ( 10) but replace (9) with

EQUATION

It should be noted that (11) trains output vectors over the whole vocabulary rather than just those items occurring in the choice set A. This is empirically significant in non-anonymized datasets such as CBT and Who-did-What where choices at test time may never have occurred as choices in the training data.

Attentive Reader. The Stanford Reader was derived from the Attentive Reader (Hermann et al., 2015) . The Attentive Reader uses α t = softmax t MLP([h t , q]) instead of ( 7). Here MLP(x) is the output of a multi layer perceptron (MLP) given input x. Also, the answer distribution in the attentive reader is defined over the full vocabulary rather than just the candidate answer set A.

EQUATION

Equation ( 12) is similar to (11) in that it leads to the training of output vectors for the full vocabulary rather than just those items appearing in choice sets in the training data. As in memory networks, this leads to improved performance on non-anonymized data sets.

3.2. EXPLICIT REFERENCE READERS:

Attention-Sum Reader. In the Attention-Sum Reader (Kadlec et al., 2016) h and q are computed with equations ( 5) and ( 6) as in the Stanford Reader but using GRUs rather than LSTMs. The attention α t is computed similarly to (7) but using a simple inner product α t = softmax t h t q rather than a trained bilinear form. Most significanlty, however, equations ( 9) and ( 10) are replaced by the following where t ∈ R(a, p) indicates that a reference to candidate answer a occurs at position t in p.

EQUATION

Here we think of R(a, p) as the set of references to a in the passage p. It is important to note that ( 13) is an equality and that P (a|p, q, A) is not normalized to the members of R(a, p). When training with the log-loss objective this drives the attention α t to be normalized -to have support only on the positions t with t ∈ R(a, p) for some a. See the heat maps in the appendix.

Gated-Attention Reader. The Gated Attention Reader Dhingra et al. ( 2016) involves a K-layer biGRU architecture defined by the following equations.

q = [fGRU(e(q)) |q| , bGRU(e(q)) 1 ] 1 ≤ ≤ K h 1 = biGRU(e(p)) h = biGRU(h -1 q -1 ) 2 ≤ ≤ K

Here the question embeddings q for different values of are computed with different GRU model parameters. Here h q abbreviates the sequence h 1 q, h 2 q, . . . h |p| q. Note that for K = 1 we have only q 1 and h 1 as in the attention-sum reader. An attention is then computed over the final layer h K with α t = softmax t (h K t ) q K in the attention-sum reader. This reader uses ( 13) and ( 14).

Attention-over-Attention Reader, The Attention-over-Attention Reader (Cui et al., 2016) uses a more elaborate method to compute the attention α t . We will use t to range over positions in the passage and j to range over positions in the question. The model is then defined by the following equations.

h = biGRU(e(p)) q = biGRU(e(q))

α t,j = softmax t h t q j β t,j = softmax j h t q j β j = 1 |p| t β t,j α t = j β j α t,j

Note that the final equation defining α t can be interpreted as applying the attention β j to the attentions α t,j . This reader uses ( 13) and ( 14).

4. EMERGENT PREDICATION STRUCTURE:

As discussed in the introduction the entity identifiers such as "entity37" introduced in the CNN/DailyMail dataset cannot be assigned any semantics other than their identity. We should think of them as pointers or semantics-free constant symbols. Despite this undermining of semantics, aggregation readers using ( 8) and ( 10) are able to perform well. Here we posit that this is due to an emergent predication structure in the hidden vectors h t . Intuitively we want to think of the hidden state vector h t as a concatenation [e(Φ t ), e o (a t )] where Φ t carries semantic information true of a t . We think of h t as representing Φ t [a t ] for semantic statement Φ t [x] asserted of the constant symbol a t . We also think of the vector representation q of the question as having the form [e(Ψ), 0] and the vector embedding e o (a) as having the form [0, e o (a)].

Unfortunately, the decomposition of h t into this predication structure need not be axis aligned.

Rather than posit an axis-aligned concatenation we posit that the hidden vector space H is a possibly non-aligned direct sum H = S ⊕ E (15) where S is a subspace of "statement vectors" and E is an orthogonal subspace of "entity pointers". Each hidden state vector h ∈ H then has a unique decomposition as h = Ψ+e for Ψ ∈ S and e ∈ E. This is equivalent to saying that the hidden vector space H is some rotation of a concatenation of the vector spaces S and E.

We now present empirical evidence for this decomposition structure. We first note that the predication decomposition implies that e o (a) h t equals e o (a) e o (a t ). This suggests the following for some fixed positive constant c. 

e o (a) h t = c if t ∈ R(a,

α t

and hence ( 10) and ( 14) agree -the aggregation readers and the explicit reference readers are using essentially the same answer selection criterion.

Empirical evidence for ( 16) is given in the first three rows of table 1. The first row empirically measures the "constant" c in ( 16) by measuring e 0 (a) h t for those cases where t ∈ R(a, p). The second row measures "0" in ( 16) by measuring e o (a) h t in those cases where t ∈ R(a, p). Additional evidence for ( 16) is given in figure 1 showing that the output vectors e o (a) for different entity identifiers a are nearly orthogonal. Orthogonality of the output vectors is required by ( 16) provided that each output vector e o (a) is in the span of the hidden state vectors h t,p for which t ∈ R(a, p).

Intuitively, the mean of all vectors h t,p with t ∈ R(a, p) should be approximately equal to e o (a). Of course empirically this will only be approximately true.

Equation ( 16) would suggest that the vector embedding of the constant symbols should have dimension at least as large as the number of distinct constants. However, in practice is sufficient that e(a) e(a ) is small for a = a . This allows the vector embeddings of the constants to have dimension much smaller than the number of constants. We have experimented with two-sparse constant symbol embeddings where the number of embedding vectors in dimention d is 2d(d -1) (d choose 2 times the four ways of setting the signs of the non-zero coordinates). Although we do not report results here, these designed and untrained constant embeddings worked reasonably well. As another testable predication we note that the posited decomposition of the hidden state vectors implies q (h i + e o (a)) = q h i .

(17) This equation is equivalent to q e o (a) = 0. Experimentally, however, we cannot expect q e o (a) to be exactly zero and ( 17) seems to provides a more experimentally meaningful test. Empirical evidence for ( 17) is given in the fourth and fifth row of table 1. The fourth row measures the cosine of the angle between the question vector q and the hidden state h t averaged over passage positions t at which some entity identifier occurs. The fifth row measures the cosine of the angle between q and e o (a) averaged over the entity identifiers a. A question asks for a value of x such that a statement Ψ[x] is implied by the passage. For a question Ψ we might even suggest the following vectorial interpretation of entailment.

Φ[x] implies Ψ[x] iff Φ Ψ ≥ ||Ψ|| 1 .

This interpretation is exactly correct if some of the dimensions of the vector space correspond to predicates, Ψ is a 0-1 vector representing a conjunction predicates, and Φ is also 0-1 on these dimensions indicating whether a predicate is implied by the context. Of course in practice one expects the dimension to be smaller than the number of possible predicates.

5. POINTER ANNOTATION READERS:

It is of course important to note that anonymization provides reference information -anonymization assumes that one can determine coreference so as to replace coreferent phrases with the same entity identifier. Anonymization allows the reference set R(a, p) to be directly read off of the passage. Still, an aggregation reader must learn to recover this explicit reference structure.

Aggregation readers can have difficulty when anonymization is not done. The Stanford Reader achieves just better than 45% on Who-did-What dataset while Attention Sum Reader can get near 60%. But if we anonymize the Who-did-What dataset and then re-train the Stanford Reader, the accuracy jumps to near 65%. Anonymization has two effects. First, it greatly reduces the number of output word e o (a) to be learned -we need only learn output embeddings for the relatively small number of entity identifiers needed. Second, anonymization suppresses the semantics of the reference phrases and leaves only a semantics-free entity identifier. This suppression of semantics may facilitate the separation of the hidden state vector space H into a direct sum S ⊕ E with q ∈ S and e o (a) ∈ E.

We can think of anonymization as providing additional linguistic input for the reader -it explicitly marks positions of candidate answers and establishes coreference. A natural question is whether Table 2: Accuracy on WDW dataset. All these results are based on single model. Results for neural readers other than NSE are on replications of those systems. All models were trained on the relaxed training set which uniformly yields better performance than the restricted training set. The first group of models are explicit reference models and the second group are aggregation models. + indicates anonymization with better reference identifier.

Who did What:

Val Test Attention Sum Reader (Onishi et al., 2016) 59.8 58.8 Gated Attention Reader (Onishi et al., 2016) 60.3 59.6 NSE (Munkhdalai & Yu, 2016) 66 this information can be provided without anonymization by simply adding additional coreference features to the input. Here we evaluate two architectures inspired by this question. This evaluation is done on the Who-did-What dataset which is not anonymized. In each architecture we add features to the input to mark the occurrences of candidate answers. These models are simpler than the Stanford reader but perform comparably. This comparable performance in table 2 further supports our analysis of logical structure in aggregation readers.

One-Hot Pointer Annotation: The Stanford Reader involves both input embeddings of words and output embeddings of entity identifiers. In the Who-did-What dataset each problem has at most five choices in the multiple choice answer list. This means that we need only five entity identifiers and we can use a five dimensional one-hot vector representation for answer identifiers. If an answer choice exists at position t in the passage let i t be the index of that choice on the choice list. If no choice occurs t take i t to be zero. Take e (i) to be the zero vector if i = 0 and otherwise to be the one-hot vector for i. We defined pointer annotation to be the result of adding e (i t ) as additional features to the input embedding. e(w t ) = [e(w t ), e (i t )]

We then define a one-hot pointer reader by designates five dimensions of the hidden state as indicators of the answer and take the probability of choice i to be defined as

EQUATION

)

where o is computed by (8).

General Pointer Annotation: In the CNN dataset there are roughly 500 entity identifier and a onehot representation is not desirable. Instead we can let e (i) be a fixed set of "pointers vectors"vectors distributed widely on the unit sphere so that for i = j we have that e (i) e (j) is small. We again use (18) but replace (19) with

EQUATION

In the general pointer reader the pointer embeddings e (i) are held fixed and not trained.

Linguistic Features. Each model can be modified to include additional input features for each input token in the question and passage. More specifically we can add the following features to the word embeddings.

• Binary feature: whether current token occurs in the question.

• Real value feature: the frequency of current token in the passage.

At a very high level our analysis and experiments support a central role for reference resolution in reading comprehension. Automating reference resolution in neural models, and demonstrating its value on appropriate datasets, would seem to be an important area for future research.

Of course there is great interest in "learning representations". The current state of the in reading comprehension is such that systems still benefit from externally provided linguistic features including externally annotated reference resolution. It would seem desirable to develop fully automated neural readers that perform as well as readers using externally provided annotations. It is of course important to avoid straw man baselines when making any such claim.

We are hesitant to make any more detailed comments on the differences between the architectural details of the readers discussed in this paper. The differences in scores between the leading readers are comparable to differences in scores that can be achieved by aggressive search over meta parameters or the statistical fluctuations in the quality of models learned by noisy statistical training procedures. More careful experiments over a longer period of time are needed. More dramatic improvements in performance would of course provide better support for particular innovations. (Saxe et al., 2013) and biases were initialized to zero. Hidden to hidden state weights were initialized by identity matrices to force the model can remember longer information. To compute the attention weight, we α t = h t W α q and initialize W α with random uniform distribution. We also used the gradient clipping (Razvan et al., 2013) with threshold of 10 and batches of size 32.

8. APPENDIX:

During training we randomly shuffled all examples within each epoch. To speedup training, we always pre-fetched 10 batches worth of examples and sorted them according to document length as did by Kadlec et al. (2016) . When trained on CNN, DailyMail and WDW (anonymization case) dataset, we randomly reshuffled the entity identifier to match the procedure proposed in Hermann et al. (2015) .

During training we evaluated the accuracy after each epoch and stopped the training when the accuracy on the validation set started decreasing. We tried limiting the vocabulary to the most frequent tokens but didn't observed any performance improvement compared with using all the distinct tokens as vocabulary. Since part of our experiments need to check the word embedding assignment issues, finally we use all the distinct tokens as vocabulary. To find the optimal embedding and hidden state dimension, we tried several groups of different combinations, the optimal value and corresponding training statistics in Gated Attention readers are summarized in Table . 4. When anonymize the Whodid-What dataset, we can either use simple string match to replace answer in question and story with entity identifier, or we can use Name Entity Recognition(NER) tools3 to detect name entities and then replace the answer name entities in question and story with entity identifier, we found the later one generally will bring 2 % improvement compared with simple string match. More experimental details can be found in code. @entity0 ( @entity1 ) six survivors of the @entity0 kosher supermarket siege in january are suing a @entity5 media outlet for what they call dangerous live broadcasting during the hostage -taking . according to @entity0 prosecutor 's spokeswoman @entity10 , the lawsuit was filed march 27 and a preliminary investigation was opened by the prosecutor 's office wednesday . the media outlet , @entity1 affiliate @entity16 , is accused of endangering the lives of the hostages , who were hiding in a cold room during the attack , by broadcasting their location live during the siege . @entity23 in a statement friday said one of its journalists " mentioned only once the presence of a woman hidden inside the @entity27 , on the basis of police sources on the ground . " " immediately , the chief editor felt that this information should not be released . it therefore has subsequently never been repeated on air or posted on -screen . @entity16 regrets that the mention of this information could cause concern to the hostages , as well as their relatives , that their lives were in danger , " the statement said . gunman @entity47 , also suspected in the slaying of a police officer , stormed the @entity27 @entity51 supermarket on january 9 , killing four people and taking others hostage . he was killed in the police operation to end the siege . a 24 -year -old supermarket employee , @entity57 -born @entity56 , was hailed as a hero afterward when it emerged that he had risked his life to hide 15 customers from @entity47 in the cold room . the hostage -taking was the culmination of three days of terror in @entity0 that began with the january 7 shooting of 12 people at the offices of @entity5 satirical magazine @entity69 . the two brothers blamed for that attack , @entity72 and @entity73 , were killed on january 9 after a violent standoff at an industrial site . the terror attacks claimed the lives of 17 people and put @entity5 on a heightened state of alert . @entity1 's @entity80 reported from @entity0 , and @entity81 wrote from @entity82 . @entity1 's @entity83 contributed to this report . query: they hid in a cold room during the attack in @entity0 by gunman @placeholder 

8.3. HEAT MAP OF DIFFERENT READERS:

We randomly choose one article from CNN dataset and show the attention map α t = softmax(q W a h t ) for different readers (in Attention Sum and Gated Attention Reader, W α is identity matrix). From figure 7 , figure 8 and figure 9 , we can see that different readers essential put the weights on the entity identifiers.

@entity0 ( @entity1 ) six survivors of the @entity0 kosher supermarket siege in january are suing a @entity5 media outlet for what they call dangerous live broadcasting during the hostage -taking . according to @entity0 prosecutor 's spokeswoman @entity10 , the lawsuit was filed march 27 and a preliminary investigation was opened by the prosecutor 's office wednesday . the media outlet , @entity1 affiliate @entity16 , is accused of endangering the lives of the hostages , who were hiding in a cold room during the attack , by broadcasting their location live during the siege . @entity23 in a statement friday said one of its journalists " mentioned only once the presence of a woman hidden inside the @entity27 , on the basis of police sources on the ground . " " immediately , the chief editor felt that this information should not be released . it therefore has subsequently never been repeated on air or posted on -screen . @entity16 regrets that the mention of this information could cause concern to the hostages , as well as their relatives , that their lives were in danger , " the statement said . gunman @entity47 , also suspected in the slaying of a police officer , stormed the @entity27 @entity51 supermarket on january 9 , killing four people and taking others hostage . he was killed in the police operation to end the siege . a 24 -year -old supermarket employee , @entity57 -born @entity56 , was hailed as a hero afterward when it emerged that he had risked his life to hide 15 customers from @entity47 in the cold room . the hostage -taking was the culmination of three days of terror in @entity0 that began with the january 7 shooting of 12 people at the offices of @entity5 satirical magazine @entity69 . the two brothers blamed for that attack , @entity72 and @entity73 , were killed on january 9 after a violent standoff at an industrial site . the terror attacks claimed the lives of 17 people and put @entity5 on a heightened state of alert . @entity1 's @entity80 reported from @entity0 , and @entity81 wrote from @entity82 . @entity1 's @entity83 contributed to this report . query: they hid in a cold room during the attack in @entity0 by gunman @placeholder @entity0 ( @entity1 ) six survivors of the @entity0 kosher supermarket siege in january are suing a @entity5 media outlet for what they call dangerous live broadcasting during the hostage -taking . according to @entity0 prosecutor 's spokeswoman @entity10 , the lawsuit was filed march 27 and a preliminary investigation was opened by the prosecutor 's office wednesday . the media outlet , @entity1 affiliate @entity16 , is accused of endangering the lives of the hostages , who were hiding in a cold room during the attack , by broadcasting their location live during the siege . @entity23 in a statement friday said one of its journalists " mentioned only once the presence of a woman hidden inside the @entity27 , on the basis of police sources on the ground . " " immediately , the chief editor felt that this information should not be released . it therefore has subsequently never been repeated on air or posted on -screen . @entity16 regrets that the mention of this information could cause concern to the hostages , as well as their relatives , that their lives were in danger , " the statement said . gunman @entity47 , also suspected in the slaying of a police officer , stormed the @entity27 @entity51 supermarket on january 9 , killing four people and taking others hostage . he was killed in the police operation to end the siege . a 24 -year -old supermarket employee , @entity57 -born @entity56 , was hailed as a hero afterward when it emerged that he had risked his life to hide 15 customers from @entity47 in the cold room . the hostage -taking was the culmination of three days of terror in @entity0 that began with the january 7 shooting of 12 people at the offices of @entity5 satirical magazine @entity69 . the two brothers blamed for that attack , @entity72 and @entity73 , were killed on january 9 after a violent standoff at an industrial site . the terror attacks claimed the lives of 17 people and put @entity5 on a heightened state of alert . @entity1 's @entity80 reported from @entity0 , and @entity81 wrote from @entity82 . @entity1 's @entity83 contributed to this report . query: they hid in a cold room during the attack in @entity0 by gunman @placeholder @entity0 ( @entity1 ) six survivors of the @entity0 kosher supermarket siege in january are suing a @entity5 media outlet for what they call dangerous live broadcasting during the hostage -taking . according to @entity0 prosecutor 's spokeswoman @entity10 , the lawsuit was filed march 27 and a preliminary investigation was opened by the prosecutor 's office wednesday . the media outlet , @entity1 affiliate @entity16 , is accused of endangering the lives of the hostages , who were hiding in a cold room during the attack , by broadcasting their location live during the siege . @entity23 in a statement friday said one of its journalists " mentioned only once the presence of a woman hidden inside the @entity27 , on the basis of police sources on the ground . " " immediately , the chief editor felt that this information should not be released . it therefore has subsequently never been repeated on air or posted on -screen . @entity16 regrets that the mention of this information could cause concern to the hostages , as well as their relatives , that their lives were in danger , " the statement said . gunman @entity47 , also suspected in the slaying of a police officer , stormed the @entity27 @entity51 supermarket on january 9 , killing four people and taking others hostage . he was killed in the police operation to end the siege . a 24 -year -old supermarket employee , @entity57 -born @entity56 , was hailed as a hero afterward when it emerged that he had risked his life to hide 15 customers from @entity47 in the cold room . the hostage -taking was the culmination of three days of terror in @entity0 that began with the january 7 shooting of 12 people at the offices of @entity5 satirical magazine @entity69 . the two brothers blamed for that attack , @entity72 and @entity73 , were killed on january 9 after a violent standoff at an industrial site . the terror attacks claimed the lives of 17 people and put @entity5 on a heightened state of alert . @entity1 's @entity80 reported from @entity0 , and @entity81 wrote from @entity82 . @entity1 's @entity83 contributed to this report . query: they hid in a cold room during the attack in @entity0 by gunman @placeholder @entity0 ( @entity1 ) six survivors of the @entity0 kosher supermarket siege in january are suing a @entity5 media outlet for what they call dangerous live broadcasting during the hostage -taking . according to @entity0 prosecutor 's spokeswoman @entity10 , the lawsuit was filed march 27 and a preliminary investigation was opened by the prosecutor 's office wednesday . the media outlet , @entity1 affiliate @entity16 , is accused of endangering the lives of the hostages , who were hiding in a cold room during the attack , by broadcasting their location live during the siege . @entity23 in a statement friday said one of its journalists " mentioned only once the presence of a woman hidden inside the @entity27 , on the basis of police sources on the ground . " " immediately , the chief editor felt that this information should not be released . it therefore has subsequently never been repeated on air or posted on -screen . @entity16 regrets that the mention of this information could cause concern to the hostages , as well as their relatives , that their lives were in danger , " the statement said . gunman @entity47 , also suspected in the slaying of a police officer , stormed the @entity27 @entity51 supermarket on january 9 , killing four people and taking others hostage . he was killed in the police operation to end the siege . a 24 -year -old supermarket employee , @entity57 -born @entity56 , was hailed as a hero afterward when it emerged that he had risked his life to hide 15 customers from @entity47 in the cold room . the hostage -taking was the culmination of three days of terror in @entity0 that began with the january 7 shooting of 12 people at the offices of @entity5 satirical magazine @entity69 . the two brothers blamed for that attack , @entity72 and @entity73 , were killed on january 9 after a violent standoff at an industrial site . the terror attacks claimed the lives of 17 people and put @entity5 on a heightened state of alert . @entity1 's @entity80 reported from @entity0 , and @entity81 wrote from @entity82 . @entity1 's @entity83 contributed to this report . query: they hid in a cold room during the attack in @entity0 by gunman @placeholder ( @entity3 ) suspected @entity2 militants this week attacked civilians inside @entity5 for the first time in a month , killing at least 16 villagers , a military spokesman told @entity3 saturday . six attackers were killed by @entity5 forces , said maj. @entity10 , an operations officer with a special military unit set up to fight @entity2 . the attackers came thursday " in the hundreds ... torched @entity14 village in the @entity15 , " he said . @entity14 is a village that borders @entity17 and has been identified as a recruiting ground for @entity2 . regional gov. @entity19 said the insurgents have been attacking border villages in @entity5 in search of supplies . @entity5 troops retook cattle that was stolen by the attackers in @entity14 , @entity10 said . the last attack in @entity5 by the @entity29 -based militants was march 10 , when the assailants struck the locality of @entity32 in a failed attempt to overrun a military base . @entity2 , whose name translates as " @entity44 education is sin , " has been waging a years -long campaign of terror aimed at instituting its extreme version of @entity42 law in @entity29 . @entity2 's tactics have intensified in recent years , from battling @entity29 government soldiers to acts disproportionately affecting civilians --such as raids on villages , mass kidnappings , assassinations , market bombings and attacks on churches and unaffiliated mosques . much of this violence has taken place in @entity29 , but neighboring countries --@entity5 included --have also been hit increasingly hard . journalist @entity61 in @entity63 , @entity5 , contributed to this report .

query: @placeholder is based in @entity29 but has attacked across the border of several neighbors ( @entity3 ) suspected @entity2 militants this week attacked civilians inside @entity5 for the first time in a month , killing at least 16 villagers , a military spokesman told @entity3 saturday . six attackers were killed by @entity5 forces , said maj. @entity10 , an operations officer with a special military unit set up to fight @entity2 . the attackers came thursday " in the hundreds ... torched @entity14 village in the @entity15 , " he said . @entity14 is a village that borders @entity17 and has been identified as a recruiting ground for @entity2 . regional gov. @entity19 said the insurgents have been attacking border villages in @entity5 in search of supplies . @entity5 troops retook cattle that was stolen by the attackers in @entity14 , @entity10 said . the last attack in @entity5 by the @entity29 -based militants was march 10 , when the assailants struck the locality of @entity32 in a failed attempt to overrun a military base . @entity2 , whose name translates as " @entity44 education is sin , " has been waging a years -long campaign of terror aimed at instituting its extreme version of @entity42 law in @entity29 . @entity2 's tactics have intensified in recent years , from battling @entity29 government soldiers to acts disproportionately affecting civilians --such as raids on villages , mass kidnappings , assassinations , market bombings and attacks on churches and unaffiliated mosques . much of this violence has taken place in @entity29 , but neighboring countries --@entity5 included --have also been hit increasingly hard . journalist @entity61 in @entity63 , @entity5 , contributed to this report .

query: @placeholder is based in @entity29 but has attacked across the border of several neighbors ( @entity3 ) suspected @entity2 militants this week attacked civilians inside @entity5 for the first time in a month , killing at least 16 villagers , a military spokesman told @entity3 saturday . six attackers were killed by @entity5 forces , said maj. @entity10 , an operations officer with a special military unit set up to fight @entity2 . the attackers came thursday " in the hundreds ... torched @entity14 village in the @entity15 , " he said . @entity14 is a village that borders @entity17 and has been identified as a recruiting ground for @entity2 . regional gov. @entity19 said the insurgents have been attacking border villages in @entity5 in search of supplies . @entity5 troops retook cattle that was stolen by the attackers in @entity14 , @entity10 said . the last attack in @entity5 by the @entity29 -based militants was march 10 , when the assailants struck the locality of @entity32 in a failed attempt to overrun a military base . @entity2 , whose name translates as " @entity44 education is sin , " has been waging a years -long campaign of terror aimed at instituting its extreme version of @entity42 law in @entity29 . @entity2 's tactics have intensified in recent years , from battling @entity29 government soldiers to acts disproportionately affecting civilians --such as raids on villages , mass kidnappings , assassinations , market bombings and attacks on churches and unaffiliated mosques . much of this violence has taken place in @entity29 , but neighboring countries --@entity5 included --have also been hit increasingly hard . journalist @entity61 in @entity63 , @entity5 , contributed to this report .

query: @placeholder is based in @entity29 but has attacked across the border of several neighbors 

:

http://nlp.stanford.edu/data/glove.6B.zip

http://nlp.stanford.edu/software/CRF-NER.shtml

Figures and Tables:

Figure 1: Plot of e o (a i ) e o (a j ) from Stanford Reader trained on CNN dataset. Off-diagonal values have mean 25.6 and variance 17.2 while diagonal values have mean 169 and variance 17.3.

Figure 2: Heat map of softmax(e o (a)h t ) when a = entity0.

Figure 3: Heat map of softmax(e o (a)h t ) when a = entity1.

Figure 4: Heat map of softmax(e o (a)h t ) when a = entity16.

Figure 5: Heat map of softmax(e o (a)h t ) when a = entity27.

Figure 6: Heat map of softmax(e o (a)h t ) when a = entity47.

Figure 7: Heat map α t for Stanford Reader

Figure 8: Heat map α t for Gated Attention Reader

Figure 9: Heat map α t for Attention Sum Reader

Statistics to support (16) and (17). These statistics are computed for the Stanford Reader.As further support for (16) we give heat maps for e o (a)h t in the appendix for different identifiers a and heat maps for α t for different readers in the appendix.

Table content: <table><tr><td/><td/><td>CNN Dev</td><td/><td/><td>CNN Test</td><td/></tr><tr><td/><td>samples</td><td colspan="2">mean variance</td><td>samples</td><td colspan="2">mean variance</td></tr><tr><td>e o (a) h t , t ∈ R(a, p)</td><td>222,001</td><td>10.66</td><td>2.26</td><td>164,746</td><td>10.70</td><td>2.45</td></tr><tr><td>e o (a) h t , t / ∈ R(a, p)</td><td colspan="2">93,072,682 -0.57</td><td>1.59</td><td colspan="2">68,451,660 -0.58</td><td>1.65</td></tr><tr><td>e o (a) h t±1 , t ∈ R(a, p)</td><td>443,878</td><td>2.32</td><td>1.79</td><td>329,366</td><td>2.25</td><td>1.84</td></tr><tr><td/><td>222,001</td><td>0.22</td><td>0.11</td><td>164,746</td><td>0.22</td><td>0.12</td></tr><tr><td>Cosine(q, e o (a)), ∀a</td><td>103,909</td><td>-0.03</td><td>0.04</td><td>78,411</td><td>-0.03</td><td>0.04</td></tr></table>

8.1 EXPERIMENT DETAILSWe implemented the neural readers using Theano (de ricBastien et al., 2012) andBlocks (van  Merrienboer et al., 2015)  and train them on single Nvidia Tesla K40 GPU. Negative log-likelihood is employed as training criterion. We used stochastic gradient descent (SGD) with the ADAM update rule(Kingma & Ba, 2015) and set the learning rate 0.0005.For Stanford Reader and One-Hot Pointer Reader, we simply follows the Stanford Reader's setting and didn't tune it on each dataset. For gated attention reader, the lookup table was randomly initialized with uniform distribution from the interval [-0.2, 0.2] on CBT dataset, but on CNN&DailyMail, the lookup table was initialized by Glove vector(Jeffrey et al., 2014) trained on the train&validatation set (we found that the pre-trained word vector doesn't improve the accuracy but will accelerate the training) on CNN&DailyMail. On WDW dataset, the lookup table was initialized by pre-trained Glove vector 2 . It should be noticed that if we initialize the lookup table with pre-trained Glove vector from //nlp.stanford.edu/data/glove.6B.zip, it will slightly boost the accuracy compared with using the Glove vector trained on train&validation set. Input to hidden state weights were initialized by random orthogonal matrices

Table content: <table/>

Training Details on Different Datasets Red color indicates larger probability and orange indicates smaller probability and the remaining indicates very low probability that can be ignored. From those figures, we can see that our assumption that e o (a) is used to pick up its occurrence is reasonable.

Table content: <table><tr><td>Dataset</td><td colspan="5">Embedding Hidden State Time Per Epoch Trained Epochs K</td></tr><tr><td>CNN</td><td>128</td><td>256</td><td>18 hours</td><td>5</td><td>3</td></tr><tr><td>DailyMail</td><td>128</td><td>256</td><td>2 days</td><td>5</td><td>3</td></tr><tr><td>WDW Relaxed</td><td>200</td><td>384</td><td>2.5 hours</td><td>8</td><td>1</td></tr><tr><td>CBT NE</td><td>384</td><td>384</td><td>1 hour</td><td>8</td><td>1</td></tr><tr><td>CBT CN</td><td>384</td><td>256</td><td>1 hour</td><td>7</td><td>1</td></tr><tr><td colspan="5">8.2 HEAT MAP OF STANFORD READER FOR DIFFERENT ANSWER CANDIDATES</td><td/></tr></table>

Peer review (iclr2017):
The paper proposed to analyze several recently developed machine readers and found that some machine readers could potentially take advantages of the entity marker (given that the same marker points out to the same entity). I usually like analysis papers, but I found the argument proposed in this paper not very clear. I like the experiments on the Stanford reader, which shows that the entity marker in fact helps the Stanford reader on WDW. I found that results rather interesting. However, I found the organization and the overall message of this paper quite confusing. First of all, it feels that the authors want to explain the above behavior with some definition of the “structures”. However, I am not sure that how successful the attempt is. For me, it is still not clear what the structures are. This makes reading section 4 a bit frustrating. I am also not sure what is the take home message of this paper. Does it mean that the entity marking should be used in the MR models? Should we design models that can also model the entity reference at the same time? What are the roles of the linguistic features here? Should we use linguistic structure to overcome the reference issue? Overall, I feel that the analysis is interesting, but I feel that the paper can benefit from having a more focused argument.