Original: M ULTI - STEP R ETRIEVER -R EADER I NTERACTION FOR S CALABLE O PEN - DOMAIN Q UESTION A NSWERING
Revised: M ULTI - STEP R ETRIEVER -R EADER I NTERACTION FOR S CALABLE O PEN - DOMAIN Q UESTION A NSWERING

Original: Anonymous authors

Original: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other.
Revised: This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other.

Original: The framework is agnostic to the architecture of the machine reading model provided it has access to the token-level hidden representations of the reader.
Revised: The framework is agnostic to the architecture of the machine reading model provided it has access to the token-level hidden representations of the reader.

Original: The retriever uses fast nearest neighbor searchalgorithms that allow it to scale to corpora containing millions of paragraphs.
Revised: The retriever uses fast nearest neighbor search that allows it to scale to corpora containing millions of paragraphs.

Original: A gated recurrent unit updates the query at each step conditioned on the “state” of the reader and the “reformulated” query is used to re-rank the paragraphs by the retriever.
Revised: A gated recurrent unit updates the query at each step conditioned on the state of the reader and the reformulated query is used to re-rank the paragraphs by the retriever.

Original: We show the efﬁcacy of our architecture by achieving state-of-the-art results (9.5% relative increase) on TriviaQA-unﬁltered

Original: and we achieve competitive performance on other large open domain datasets such as

Original: Q UASAR -T, S EARCH QA, and SQ U AD-open.

Original: We conduct analysis and show that iterative interaction helps in retrieving useful paragraphs from the corpus.
Revised: We conduct analysis and show that iterative interaction helps in retrieving informative paragraphs from the corpus.

Original: Finally, we show that our multi-step-reasoning framework brings uniform improvements when applied to two widely used reader architectures – Dr.QA and BiDAF 1 .
Revised: Finally, we show that our multistep-reasoning framework brings consistent improvement when applied to two widely used reader architectures (D R . QA and BIDAF ) on various large open-domain datasets —T RIVIAQA -unﬁltered, Q UASAR - T , S EARCHQA , and S QUAD -open 1 .

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Open-domain question answering (QA) (Voorhees et al., 1999) involves a retriever for selecting relevant context from a large corpora of text (e g. Wikipedia) and a reader for ‘reasoning’ on the retrieved context.
Revised: Open-domain question answering (QA) (Voorhees et al., 1999) involves a retriever for selecting relevant context from a large corpora of text (e g. Wikipedia) and a machine reading comprehension (MRC) model for ‘reasoning’ on the retrieved context.

Original: A lot of effort has been put into designing sophisticated neural machine reading architectures for reading short context (e g. a single paragraph), with much success (Wang & Jiang, 2017; Seo et al., 2017; Xiong et al., 2017; Wang et al., 2018c; Yu et al., 2018, inter alia).
Revised: A lot of effort has been put into designing sophisticated neural MRC architectures for reading short context (e g. a single paragraph), with much success (Wang & Jiang, 2017; Seo et al., 2017; Xiong et al., 2017; Wang et al., 2018c; Yu et al., 2018, inter alia).

Original: However, the performance of such systems degrade signiﬁcantly when combined with a retriever in open domain settings (Chen et al., 2017).
Revised: However, the performance of such systems degrades signiﬁcantly when combined with a retriever in open domain settings.

Original: For example, the exact match accuracy of DrQA (Chen et al., 2017), on the SQ U AD dataset (Rajpurkar et al., 2016) degrades from 69.5% to 28.4% in open-domain settings.
Revised: For example, the exact match accuracy of DrQA (Chen et al., 2017), on the S QUAD dataset (Rajpurkar et al., 2016) degrades from 69.5% to 28.4% in open-domain settings.

Original: The primary reason for this degradation in performance is due to the retriever’s failure to ﬁnd the relevant paragraphs for the machine reading model (Htut et al., 2018).
Revised: The primary reason for this degradation in performance is due to the retriever’s failure to ﬁnd the relevant paragraphs for the machine reading model (Htut et al., 2018).

Original: We propose the following  desiderata for a general purpose open-domain QA system - (a) The retriever model should be fast , since it has to ﬁnd the relevant context from a very large text corpora and give it to the more sophisticated and computationally expensive machine reading model (b)
Revised: We propose the following two desiderata for a general purpose open-domain QA system - (a) The retriever model should be fast , since it has to ﬁnd the relevant context from a very large text corpora and give it to the more sophisticated and computationally expensive MRC model (b)

Original: Secondly, the retriever and reader models should be interactive, i.e. if the reader model is unable to ﬁnd the answer from the initial retrieved context, the retriever should be able to learn to provide more relevant context to the reader.
Revised: Secondly, the retriever and reader models should be interactive, i.e. if the reader model is unable to ﬁnd the answer from the initial retrieved context, the retriever should be able to learn to provide more relevant context to the reader.

Original: Open-domain QA systems such as R 3 (Wang et al., 2018a) and DS-QA (Lin et al., 2018) have sophisticated retriever models where the reader and retriever are jointly trained.
Revised: Open-domain QA systems such as R 3 (Wang et al., 2018a) and DS-QA (Lin et al., 2018) have sophisticated retriever models where the reader and retriever are jointly trained.

Original: However, their retriever computes question-dependent paragraph representation which is then encoded by running an expensive recurrent neural network over the tokens in the paragraph.
Revised: However, their retriever computes question-dependent paragraph representation which is then encoded by running an expensive recurrent neural network over the tokens in the paragraph.

Original: Since the retriever has to rank a lot of paragraphs, this design would not scalewell to large corporas.
Revised: Since the retriever has to rank a lot of paragraphs, this design does not scale to large corporas.

Original: One the other hand, the retriever model of QA systems such as DrQA (Chen et al., 2017), Clark & Gardner (2018) are based on a tf-idf retriever, but they lack trainable parameters and are consequently unable to recover from mistakes.
Revised: One the other hand, the retriever model of QA systems such as DrQA (Chen et al., 2017) is based on a tf-idf retriever, but they lack trainable parameters and are consequently unable to recover from mistakes.

Original: This paper introduces an open domain architecture in which the retriever and reader iteratively interact with each other.
Revised: This paper introduces an open domain architecture in which the retriever and reader iteratively interact with each other.

Original: Our model ﬁrst pre-computes and caches representation of context (paragraph).
Revised: Our model ﬁrst pre-computes and caches representation of context (paragraph).

Original: These representations are independent of the query unlike recent architectures (Seo et al., 2017;

Original: Xiong et al., 2017; Wang et al., 2018a; Lin et al., 2018) and hence can be computed and stored ofﬂine.

Original: [Figure]
Revised: [Figure]

Original: E m b e d q u e r y
Revised: E m b e d q u e r y

Original: [Figure]
Revised: [Figure]

Revised: These representations are independent of the query unlike recent architectures (Wang et al., 2018a;

Revised: Lin et al., 2018) and hence can be computed and stored ofﬂine.

Original: Given an input question, the retriever performs fast inner product search to ﬁnd the most relevant contexts.
Revised: Given an input question, the retriever performs fast inner product search to ﬁnd the most relevant contexts.

Original: The highest ranked contexts are then passed to the neural machine reader which usually is a multi-layer recurrent neural network with attention.
Revised: The highest ranked contexts are then passed to the neural MRC model.

Original: Our architecture is agnostic to the choice of the reader architecture and we show that multi-step-reasoning increases performance of  state-of-the-art machine reading architectures - DrQA
Revised: Our architecture is agnostic to the choice of the reader architecture and we show that multi-step-reasoning increases performance of two state-of-the-art MRC architectures - DrQA

Original: (Chen et al., 2017) and BiDAF (Seo et al., 2017).
Revised: (Chen et al., 2017) and BiDAF (Seo et al., 2017).

Original: It is possible that the answer might not exist in the initial retrieved context or that the model would need to combine information across multiple contexts (Wang et al., 2018b).
Revised: It is possible that the answer might not exist in the initial retrieved paragraphs or that the model would need to combine information across multiple paragraphs (Wang et al., 2018b).

Original: We equip the reader with an additional gated recurrent unit (Cho et al.,2014; Chung et al., 2014) which takes in the state of the reader and the current query vector and generates a new query vector .
Revised: We equip the reader with an additional gated recurrent unit (Cho et al., 2014) which takes in the state of the reader and the current query vector and generates a new query vector.

Original: This new query vector is then used by the retriever model to re-rank the context.
Revised: This new query vector is then used by the retriever model to re-rank the context.

Original: This allows the model to read new contexts and combine evidence across multiple contexts, if required.
Revised: This allows the model to read new paragraphs and combine evidence across multiple paragraphs.

Original: Since the retriever makes a ‘hard selection’ of context to send to the reader, we train the retriever and the reader jointly using reinforcement learning (RL).
Revised: Since the retriever makes a ‘hard selection’ of paragraphs to send to the reader, we train the retriever and the reader jointly using reinforcement learning.

Original: Recently, Buck et al.

Original: (2018) introduced the ActiveQA model in which a RL agent sits between the user and the QA system and learns to reformulate the natural language query so as to maximize performance.

Original: However, reformulating the query in a natural language domain is a hard problem as we do not have a good generative model for natural language.

Original: In our approach, we circumvent this issue by performing query reformulation in the continuous embedding space and not in the space of natural language.

Original: Empirically, we ﬁnd that our model signiﬁcantly outperforms ActiveQA.

Original: Our architecture draws inspiration from how students are instructed to take reading comprehension tests (Cunningham & Shablak, 1975; Bishop et al., 2006; Duggan & Payne, 2009).
Revised: Our architecture draws inspiration from how students are instructed to take reading comprehension tests (Cunningham & Shablak, 1975; Bishop et al., 2006; Duggan & Payne, 2009).

Original: Given a document containing multiple paragraphs and a set of questions which can be answered from the document, (a) the student quickly skims the paragraphs, (b) then for each question, she ﬁnds the most relevant paragraphs that she thinks will answer the question.
Revised: Given a document containing multiple paragraphs and a set of questions which can be answered from the document, (a) the student quickly skims the paragraphs, (b) then for each question, she ﬁnds the most relevant paragraphs that she thinks will answer the question.

Original: (c)
Revised: (c)

Original: She then carefully reads the chosen paragraph to answer the question (d)
Revised: She then carefully reads the chosen paragraph to answer the question (d)

Original: However, if the chosen paragraph does not answer the question, then given the question and the knowledge of what she has read till now, she decides which paragraph to read next.
Revised: However, if the chosen paragraph does not answer the question, then given the question and the knowledge of what she has read till now, she decides which paragraph to read next.

Original: Step (a) is akin to our model encoding and storing the question independent paragraph representations and step (b) corresponds to the inner product search to ﬁnd the relevant context.
Revised: Step (a) is akin to our model encoding and storing the question independent paragraph representations and step (b) corresponds to the inner product search to ﬁnd the relevant context.

Original: The reading of the context by the sophisticated neural machine reader corresponds to step (c) and the last step corresponds to the iterative (multi-step) interaction between the retriever and the reader.
Revised: The reading of the context by the sophisticated neural machine reader corresponds to step (c) and the last step corresponds to the iterative (multi-step) interaction between the retriever and the reader.

Original: To summarize, this paper makes the following contributions: (a) We introduce a new framework for open-domain QA in which the retriever and reader iteratively interact with each other via a novel multi-step-reasoning component which learns to reformulate the query vector.
Revised: To summarize, this paper makes the following contributions: (a) We introduce a new framework for open-domain QA in which the retriever and reader iteratively interact with each other via a novel multistep-reasoning component allowing it to retrieve and combine information from multiple paragraphs.

Original: (b) Unlike several recent open-domain models, our paragraph vectors are independent of the queryrepresentations which makes our architecture highly scalable and we empirically demonstrate it by running large scale experiments over millions of paragraphs.
Revised: (b) Our paragraph representations are independent of the query which makes our architecture highly scalable and we empirically demonstrate it by running large scale experiments over millions of paragraphs.

Original: (c) Lastly, our framework is agnostic to the architecture of the reader and we demonstrate the efﬁcacy of our framework on two different neuralmachine reading comprehension models.
Revised: (c) Lastly, our framework is agnostic to the architecture of the reader and we show improvements on two widely used neural reading comprehension models.

Original:  B ASIC C OMPONENTS OF OUR M ODEL
Revised:  B ASIC C OMPONENTS OF OUR M ODEL

Original: The architecture of our model consists of three main components - (a) paragraph retriever - that computes a relevance score for each paragraph w.r.t a given query and ranks them according to the computed score.
Revised: The architecture of our model consists of three main components - (a) paragraph retriever - that computes a relevance score for each paragraph w.r.t a given query and ranks them according to the computed score.

Original: (b) reader - a more sophisticated neural machine reading model that receives few top-ranked paragraphs from the retriever and outputs a span of text as a possible answer to the query and (c) multi-step-reasoner - a gated recurrent unit that facilitates iterative interaction between the retriever and the reader.
Revised: (b) reader - a more sophisticated neural machine reading model that receives few top-ranked paragraphs from the retriever and outputs a span of text as a possible answer to the query and (c) multi-step-reasoner - a gated recurrent unit that facilitates iterative interaction between the retriever and the reader.

Original: Formally, the input to our model is a natural language question Q = q 1 , q 2 ,..., q n consisting of n tokens and a set of paragraphs P = { p 1 , p 2 , ...
Revised: Formally, the input to our model is a natural language question Q = q 1 , q 2 ,..., q n consisting of n tokens and a set of paragraphs P = { p 1 , p 2 , ...

Original: p K } .
Revised: p K } .

Original: Our model extracts a span of text a as the answer to the question from the paragraphs in P. Note that the set of paragraphs in P can be the paragraphs in a set of documents retrieved by a search engine(as in T RIVIA QA web-open (Joshi et al., 2017)) or it could be all the paragraphs in a large text corpus such as Wikipedia.
Revised: Our model extracts a span of text a as the answer to the question from the paragraphs in P. Note that the set of paragraphs in P can be the paragraphs in a set of documents retrieved by a search engine or it could be all the paragraphs in a large text corpus such as Wikipedia.

Original: Next we describe each individual component of our model.
Revised: Next we describe each individual component of our model.

Original: 2. P ARAGRAPH R ETRIEVER
Revised: 2. P ARAGRAPH R ETRIEVER

Original: The paragraph retriever computes a score for how likely a paragraph is to contain an answer to a given question.
Revised: The paragraph retriever computes a score for how likely a paragraph is to contain an answer to a given question.

Original: The paragraph representations are computed independent of the query and once computed, they are not updated.
Revised: The paragraph representations are computed independent of the query and once computed, they are not updated.

Original: This allows us to cache the representations and store them ofﬂine.
Revised: This allows us to cache the representations and store them ofﬂine.

Original: The relevance score of a paragraph is computed as a inner product between the paragraph and the query vectors.
Revised: The relevance score of a paragraph is computed as a inner product between the paragraph and the query vectors.

Original: The paragraph and query representations are computed as follows.
Revised: The paragraph and query representations are computed as follows.

Original: Given a paragraph p = { p 1 , p 2 ,..., p m } consisting of m tokens, a multi-layer recurrent neural network encodes each tokens in the paragraph — { p 1 , p 2 ,..., p m } =
Revised: Given a paragraph p = { p 1 , p 2 ,..., p m } consisting of m tokens, a multi-layer recurrent neural network encodes each tokens in the paragraph — { p 1 , p 2 ,..., p m } =

Original: RNN ( { p 1 , p 2 ,..., p m } ) , where p j ∈ R 2 d encodes useful contextual information around the j -th token.
Revised: RNN ( { p 1 , p 2 ,..., p m } ) , where p j ∈ R 2 d encodes useful contextual information around the j -th token.

Original: Speciﬁcally we choose to use a multilayer bidirectional long-short term memory network (LSTM) (Hochreiter & Schmidhuber, 1997) and take p j as the hidden units in the last layer of the RNN.
Revised: Speciﬁcally we choose to use a multilayer bidirectional long-short term memory network (LSTM) (Hochreiter & Schmidhuber, 1997) and take p j as the hidden units in the last layer of the RNN.

Original: We concatenate the representation computed by the forward and the backward LSTM.
Revised: We concatenate the representation computed by the forward and the backward LSTM.

Original: To compute a single paragraph vector p ∈ R 2 d from all the token representations, we combine them using weights b j ∈ R
Revised: To compute a single paragraph vector p ∈ R 2 d from all the token representations, we combine them using weights b j ∈ R

Original: [Equation]
Revised: [Equation]

Original: Here b j encodes the importance of each token and w ∈ R 2 d , W s ∈ R 2 d × 2 d are learned weights.
Revised: Here b j encodes the importance of each token and w ∈ R 2 d , W s ∈ R 2 d × 2 d are learned weights.

Original: This is similar to computing self-attention (Lin et al., 2017; Vaswani et al., 2017).

Original: The query q = { q 1 , q 2 ,..., q n } is encoded by another network with theexact same architecture to obtain a query vector q ∈ R 2 d .
Revised: The query q = { q 1 , q 2 ,..., q n } is encoded by another network with the same architecture to obtain a query vector q ∈ R 2 d .

Original: Next the relevance score of a paragraph w.r.t the query ( score ( p , q ) ∈ R ) is computed by a simple inner product — score ( p , q )
Revised: Next the relevance score of a paragraph w.r.t the query ( score ( p , q ) ∈ R ) is computed by a simple inner product — score ( p , q )

Original: = (cid:104) p , q (cid:105) .
Revised: = (cid:104) p , q (cid:105) .

Original: The paragraph retriever then returns the top scoring k paragraphs to the reader.
Revised: The paragraph retriever then returns the top scoring k paragraphs to the reader.

Original: Fast inner product search.
Revised: Fast inner product search.

Original: Inner product can be efﬁciently computed on a GPU, however in our experiments with large corpus with over million paragraphs, (a) the paragraph vectors require more memory than available in a single commodity GPU (b) it is computationally wasteful to compute inner products with all paragraph vectors and we can do better by leveraging fast nearest neighbor (NN) search algorithms.
Revised: Inner product can be efﬁciently computed on a GPU, however in our experiments with large corpus with over million paragraphs, (a) the paragraph vectors require more memory than available in a single commodity GPU (b) it is computationally wasteful to compute inner products with all paragraph vectors and we can do better by leveraging fast nearest neighbor (NN) search algorithms.

Original: Since the retriever has to ﬁnd the k paragraphs with the highest inner products w.r.t query, our problem essentially reduces to maximum inner product search (MIPS).
Revised: Since the retriever has to ﬁnd the k paragraphs with the highest inner products w.r.t query, our problem essentially reduces to maximum inner product search (MIPS).

Original: There exists data structures for fast NN search in metric spaces such as Cover Trees (Beygelzimer et al., 2006).
Revised: There exists data structures for fast NN search in metric spaces such as Cover Trees (Beygelzimer et al., 2006).

Original: But we cannot use them directly, since triangle equality does not hold in inner product space.
Revised: But we cannot use them directly, since triangle equality does not hold in inner product space.

Original: Borrowing ideas from Bachrach et al.
Revised: Borrowing ideas from Bachrach et al.

Original: (2014); Anonymous (2018), we propose to use nearest neighbor (NN) search algorithms to perform the MIPS in sublinear time as follows.
Revised: (2014); Zaheer et al. (2019), we propose to use nearest neighbor (NN) search algorithms to perform the MIPS in sublinear time as follows.

Original: Let u be an upper bound for the L2 norm, i.e. u ≥ (cid:107) p (cid:107) , ∀ p .
Revised: Let u be an upper bound for the L2 norm for all paragraphs, i.e. u ≥ (cid:107) p (cid:107) , ∀ p .

Original: Then, the trick is to perform NN search in L2 distance with  modiﬁedquery and paragraph vectorsso that the search is equivalent to MIPS for original query.
Revised: Next, we modify the original paragraph and query vectors such that searching for the k-nearest neighbor w.r.t L2 distance with the modiﬁed vectors is equivalent to ﬁnding the k nearest (original) paragraph vectors in the inner-product space.

Original: In this regard, deﬁne the augmented paragraph vectors as ˜ p i 
Revised: Deﬁne the augmented paragraph vectors as ˜p i =

Original: = [ p i ; (cid:112) u 2 −(cid:107) p i (cid:107) 2 ] and augmented query vector as ˜ q =
Revised: [ p i ; (cid:112) u 2 −(cid:107) p i (cid:107) 2 ] and augmented query vector as ˜ q =

Original: [ q ;0 ] .
Revised: [ q ;0 ] , where [ ; ] denotes concatenation.

Original: With this transformation, we can see that:
Revised: Note, with this transformation, (cid:104) ˜ p i , ˜ q (cid:105) = (cid:104) p i , q (cid:105) .

Revised: Now,

Original: [Equation]
Revised: [Equation]

Original: Many exact NN search algorithms can ﬁnd k-NN in time (sublinear) logarithmic in number of paragraphs (Beygelzimer et al., 2006; Anonymous, 2018), after an one-time preprocessing.
Revised: Many exact NN search algorithms can ﬁnd k-NN in time (sublinear) logarithmic in number of paragraphs (Beygelzimer et al., 2006; Zaheer et al., 2019), after an one-time preprocessing.

Original: In our architecture, the preprocessing can be done because the set of all paragraphs is ﬁxed for all the queries.
Revised: In our architecture, the preprocessing can be done because the set of all paragraphs is ﬁxed for all the queries.

Original: We chose to use SGTree (Anonymous, 2018) to perform the NN/MIPS due to its fast construction time and competitive performance reducing the search to logarithmic time per query.
Revised: We chose to use SGTree (Zaheer et al., 2019) to perform the NN/MIPS due to its fast construction time and competitive performance reducing the search to logarithmic time per query.

Original: Note that our paragraph representations are independent of the given query and are never updated once they are trained.
Revised: Note that our paragraph representations are independent of the given query and are never updated once they are trained.

Original: After training is completed, we cache the p ∈ R 2 d vectors.
Revised: After training is completed, we cache the p ∈ R 2 d vectors.

Original: This is unlike many recent work in open-domain QA.
Revised: This is unlike many recent work in open-domain QA.

Original: For example, in R 3 (Wang et al., 2018a), the retriever uses
Revised: For example, in R 3 (Wang et al., 2018a), the retriever uses

Original: Match-LSTM model (Wang & Jiang, 2017) to compute question-matching representations for each token in the passage.
Revised: Match-LSTM model (Wang & Jiang, 2017) to compute question-matching representations for each token in the passage.

Original: The paragraph representations are then obtained by running a bi-directional
Revised: The paragraph representations are then obtained by running a bi-directional

Original: RNN over these matched representation.
Revised: RNN over these matched representation.

Original: Although powerful, this architecture design will not scale in open-domain settings where the retriever has to re-compute new representations for possibly millions of paragraphs for every new question.
Revised: Although powerful, this architecture design will not scale in open-domain settings where the retriever has to re-compute new representations for possibly millions of paragraphs for every new question.

Original: In contrast, once training is over, we cache the paragraph representation and use it for every query at test time.
Revised: In contrast, once training is over, we cache the paragraph representation and use it for every query at test time.

Original: Training - Following previous work (Htut et al., 2018; Lin et al., 2018; Wang et al., 2018a), we gather labels for paragraphs during training using distant supervision (Mintz et al., 2009).
Revised: Training - Following previous work (Htut et al., 2018; Lin et al., 2018; Wang et al., 2018a), we gather labels for paragraphs during training using distant supervision (Mintz et al., 2009).

Original: A paragraph that contains the exact ground truth answer string is labeled as an positive example.
Revised: A paragraph that contains the exact ground truth answer string is labeled as an positive example.

Original: For a positive (negative) labeled paragraph, we maximize (minimize) the log ( σ ( score ( p , q ))) .
Revised: For a positive (negative) labeled paragraph, we maximize (minimize) the log ( σ ( score ( p , q ))) .

Original: The number of layers of the bi-directional LSTM encoder is set to three and we use Adam (Kingma & Ba, 2014) for optimization.
Revised: The number of layers of the bi-directional LSTM encoder is set to three and we use Adam (Kingma & Ba, 2014) for optimization.

Original: Once training is done, we pre-compute and cache the paragraph representation of each dataset in our experiments.
Revised: Once training is done, we pre-compute and cache the paragraph representation of each dataset in our experiments.

Original: 2. M ACHINE R EADER
Revised: 2. M ACHINE R EADER

Original: The reader is a sophisticated neural machine reading comprehension (MRC) model that takes in the top few paragraphs sent by the retriever and outputs a span of answer text.
Revised: The reader is a sophisticated neural machine reading comprehension (MRC) model that takes in the top few paragraphs sent by the retriever and outputs a span of answer text.

Original: Our model is agnostic to the exact architecture of the reader and we perform experiments to show the efﬁcacy of our model on two state-of-the-art neural machine reading models - DrQA(Chen et al., 2017) and BiDAF (Seo et al., 2017).
Revised: Our model is agnostic to the exact architecture of the reader and we perform experiments to show the efﬁcacy of our model on two state-of-the-art neural machine reading models - DrQA and BiDAF.

Original: MRC models designed for SQ U AD (Rajpurkar et al., 2016), NewsQA (Trischler et al., 2016), etc operate on a single paragraph.
Revised: MRC models designed for S QUAD (Rajpurkar et al., 2016), NewsQA (Trischler et al., 2016), etc operate on a single paragraph.

Original: However it has been shown that reading and aggregating evidence across multiple paragraphs is important when doing QA over larger evidence set (e.g. full Wikipedia document) (Swayamdipta et al., 2018; Clark & Gardner, 2018) and in open domain settings (Wang et al., 2018b).
Revised: However it has been shown that reading and aggregating evidence across multiple paragraphs is important when doing QA over larger evidence set (e.g. full Wikipedia document) (Swayamdipta et al., 2018; Clark & Gardner, 2018) and in open domain settings (Wang et al., 2018b).

Original: Most MRC models compute a start and end scores for each token in the paragraph that represents how likely a token is the start/end of an answer span.
Revised: Most MRC models compute a start and end scores for each token in the paragraph that represents how likely a token is the start/end of an answer span.

Original: To gather evidence across multiple paragraphs sent by the retriever, we normalize the start/end scores across the paragraphs.
Revised: To gather evidence across multiple paragraphs sent by the retriever, we normalize the start/end scores across the paragraphs.

Original: Furthermore a text span can appear multiple times in a paragraph.
Revised: Furthermore a text span can appear multiple times in a paragraph.

Original: To give importance to all answer spans in the text, our objective aggregates (sums) the log-probability of the score for each answer position.
Revised: To give importance to all answer spans in the text, our objective aggregates (sums) the log-probability of the score for each answer position.

Original: In other words, let I ( w , p ) denote the token start positions where the answer span appears in the paragraph p and let w S be the starting word of the answer span.
Revised: Let I ( w , p ) denote the token start positions where the answer span appears in the paragraph p and let w S be the starting word of the answer span.

Original: Our model maximizes the sum of the following objective for the start and end word of an answer spans as follows.
Revised: Our model maximizes the sum of the following objective for the start and end word of an answer spans as follows.

Original: (For brevity, we only show the objective for the starting word (w s ) of the span.)
Revised: (For brevity, we only show the objective for the starting word (w s ) of the span.)

Original: [Equation]
Revised: [Equation]

Original: Here, P denotes the set of all top-ranked paragraphs by the retriever, n j denotes the number of tokens in paragraph j and score start ( k , j ) denotes the start score of the k -th token in the j -th paragraph.
Revised: Here, P denotes the set of all top-ranked paragraphs by the retriever, n j denotes the number of tokens in paragraph j and score start ( k , j ) denotes the start score of the k -th token in the j -th paragraph.

Original: A similar score aggregation strategy has been used in previous work (Kadlec et al., 2016; Clark & Gardner, 2018).
Revised: Score aggregation during inference.

Original: During inference, following Chen et al.
Revised: During inference, following Chen et al.

Original: ; Seo et al.
Revised: ; Seo et al.

Original: (2017), the score of a span that starts at token position i and ends at position j of paragraph p is given by the sum of the score start ( i , p ) and score end ( j , p ) .
Revised: (2017), the score of a span that starts at token position i and ends at position j of paragraph p is given by the sum of the score start ( i , p ) and score end ( j , p ) .

Original: During inference, we score spans up to a pre-deﬁned maximum length of 15.
Revised: During inference, we score spans up to a pre-deﬁned maximum length of 15.

Original: Further, we add the scores ofthe spans across different paragraphs  if they have the same surface form.
Revised: Further, we add the scores of spans across different paragraphs (even if they are retrieved at different steps), if they have the same surface form.

Original: That is, if the same span (e.g. “Barack Obama”) occurs multiple times in several paragraphs, we sum the individual span scores.
Revised: That is, if the same span (e.g. “Barack Obama”) occurs multiple times in several paragraphs, we sum the individual span scores.

Original: However, to ensure tractability, we only consider the top 10  spans  for aggregation.
Revised: However, to ensure tractability, we only consider the top 10 scoring spans in each paragraph for aggregation.

Original: It should be noted that the changes we made to aggregate evidence over multiple paragraphs and mentions of spans needs no change in the original architecture of the machine reading model.
Revised: It should be noted that the changes we made to aggregate evidence over multiple paragraphs and mentions of spans needs no change to the original MRC architecture.

Original:  M ULTI -S TEP -R EASONER
Revised:  M ULTI -S TEP -R EASONER

Original: A novel component of our open-domain architecture is the multi-step-reasoner which facilitates iterative interaction between the retriever and the machine reader.
Revised: A novel component of our open-domain architecture is the multi-step-reasoner which facilitates iterative interaction between the retriever and the machine reader.

Original: The multi-step-reasoner is a gated recurrent unit (Cho et al.,2014; Chung et al., 2014) which takes in the current state of the reader and the current query vector and does a gated update to produce a reformulated query.
Revised: The multi-step-reasoner a module comprised of a gated recurrent unit (Cho et al., 2014) which takes in the current state of the reader and the current query vector and does a gated update to produce a reformulated query.

Original: The reformulated query is sent back to the retriever which uses it to re-rank the paragraphs in the corpus.
Revised: The reformulated query is sent back to the retriever which uses it to re-rank the paragraphs in the corpus.

Original: Since the gated update of the multi-step-reasoner, conditions on the current state of the machine reader, this multi-step interaction provides a way for the search engine (retriever) and the QA model (reader) to communicate with each other.
Revised: Since the gated update of the multi-step-reasoner, conditions on the current state of the machine reader, this multi-step interaction provides a way for the search engine (retriever) and the QA model (reader) to communicate with each other.

Original: This can  be seen as an instance of two agents cooperating via communication to solve a task (Lazaridou et al., 2017; Lee et al., 2018; Cao et al., 2018).
Revised: This can also be seen as an instance of two agents cooperating via communication to solve a task (Lazaridou et al., 2017; Lee et al., 2018; Cao et al., 2018).

Original: More formally, let q t ∈ R 2 d be the current query representation which was most recently used by the paragraph retriever to score the paragraphs in the corpus.
Revised: More formally, let q t ∈ R 2 d be the current query representation which was most recently used by the paragraph retriever to score the paragraphs in the corpus.

Original: The multi-step-reasoner also has access to the reader state which is computed from the hidden memory vectors of the reader.
Revised: The multi-step-reasoner also has access to the reader state which is computed from the hidden memory vectors of the reader.

Original: The reader state captures the current information that the reader has encoded after reading the paragraphs that was sent by the retriever.
Revised: The reader state captures the current information that the reader has encoded after reading the paragraphs that was sent by the retriever.

Original: Next we show how the reader state is computed.
Revised: Next we show how the reader state is computed.

Original: The span extractive machine reading architectures compute a hidden representation for each token in the paragraph.
Revised: Span extractive machine reading architectures compute a hidden representation for each token in the paragraph.

Original: A majority of them use RNN encoders, however recent successful models also use

Original: CNN (Yu et al., 2018; Swayamdipta et al., 2018) and Transformer (Devlin et al., 2018) architectures.

Original: To compute the reader state, our framework needs access to the hidden representation for each token in the paragraph computed by the reader.
Revised: Our framework needs access to these hidden representations to compute the reader state.

Original: Let m j ∈ R 2 p be the hidden vector associated with the j -th token in the paragraph.
Revised: Let m j ∈ R 2 p be the hidden vector associated with the j -th token in the paragraph.

Original: Let L ∈ R 2 p be the ﬁnal query representation of the reader model.
Revised: Let L ∈ R 2 p be the ﬁnal query representation of the reader model.

Original: L is usually created by some pooling operation on the hidden representation of each question token.
Revised: L is usually created by some pooling operation on the hidden representation of each question token.

Original: The reader state S ∈ R 2 p is computed from each of the hidden vectors m j and L by ﬁrst computing soft-attention weights between each paragraph token, followed by combining each m j with the soft attention weights.
Revised: The reader state S ∈ R 2 p is computed from each of the hidden vectors m j and L by ﬁrst computing soft-attention weights between each paragraph token, followed by combining each m j with the soft attention weights.

Original: [Equation]
Revised: [Equation]

Original: Finally, the new reformulated query q t + 1  for the paragraph retriever is calculated by a GRU update —q t + 1 =
Revised: Finally, the new reformulated query q t + 1 ∈ R 2 d for the paragraph retriever is calculated by the multi-step-reasoner module as follows —

Original:  GRU ( q t , S ) .
Revised: [Equation] In our architecture, we used a 3 layer GRU network, followed by a one layer feed forward network (FFN) with a ReLU non-linearity.

Original: The gated update ensures that relevant information from q t is preserved and new and useful information from the reader state S is added to the reformulated query.
Revised: The gated update ensures that relevant information from q t is preserved and new and useful information from the reader state S is added to the reformulated query.

Original: 3. T RAINING
Revised: 3. T RAINING

Original: There exists no supervision for training the query reformulation of the multi-step-reasoner.
Revised: There exists no supervision for training the query reformulation of the multi-step-reasoner.

Original: Therefore, to train the parameters of the GRU network we employ reinforcement learning .
Revised: Instead, we employ reinforcement learning (RL) and train it by how well the reader performs after reading the new set of paragraphs retrieved by the modiﬁed query.

Original: We deﬁne the problem as a deterministic ﬁnite horizon Partially Observed Markov decision process (POMDP).
Revised: We deﬁne the problem as a deterministic ﬁnite horizon Partially Observed Markov decision process (POMDP).

Original: The components of POMDP are described as follows:
Revised: The components of POMDP are —

Original: States .
Revised: States .

Original: A state in the state space consists of the entire text corpora, the query, the answer and k selected paragraphs.
Revised: A state in the state space consists of the entire text corpora, the query, the answer, and k selected paragraphs.

Original: The reader is part of the environment and is fully described given the current 
Revised: The reader is part of the environment and is fully described given the current do 3: { p ← M retriever ← ←

Original:  state, i.e. selected paragraphs and the query.
Revised: : end for return score state, i.e. selected paragraphs and the query.

Original: Observations .
Revised: Observations .

Original: The agent only observes a function of the current state.
Revised: The agent only observes a function of the current state.

Original: In particular, to the agent only theinitial query vector and the memory of the reader model is shown, which are a function of the current state.
Revised: In particular, to the agent only the query vector and the memory of the reader model is shown, which are a function of the current state.

Original: Intuitively, this represents the information encoded by the machine reader model after reading the top k paragraphs sent by the retriever in current step.
Revised: Intuitively, this represents the information encoded by the machine reader model after reading the top k paragraphs sent by the retriever in current step.

Original: Actions .
Revised: Actions .

Original: The set of all paragraphs in the text corpora forms the action space.
Revised: The set of all paragraphs in the text corpora forms the action space.

Original: The retriever scores all paragraphs w.r.t the current query and selects the top k paragraphs to send to the reader model.
Revised: The retriever scores all paragraphs w.r.t the current query and selects the top k paragraphs to send to the reader model.

Original: We treat k as a hyper-parameter in our experiments.
Revised: We treat k as a hyper-parameter in our experiments.

Original: Reward .
Revised: Reward .

Original: At every step, the reward is measured by how well the answer extracted by the reader model matches to the ground-truth answer.
Revised: At every step, the reward is measured by how well the answer extracted by the reader model matches to the ground-truth answer.

Original: We use the F 1 score (calculated by word overlap between 
Revised: We use the F 1 score (calculated by word overlap between prediction and ground-truth) as the reward at each step.

Original: Transition .
Revised: Transition .

Original: The environment evolves deterministically after reading the paragraphs sent by the retriever.
Revised: The environment evolves deterministically after reading the paragraphs sent by the retriever.

Original: Our policy π is parameterized by the GRU  of the multi-step-reasoner.
Revised: Our policy π is parameterized by the GRU and the FFN of the multi-step-reasoner.

Original: Let r t denote the reward ( Fscore) returned by the environment at time t .
Revised: Let r t denote the reward ( F 1 score) returned by the environment at time t .

Original: We directly optimize the parameters to maximize the expected reward given by — J ( θ ) =
Revised: We directly optimize the parameters to maximize the expected reward given by — J ( θ ) =

Original: E π (cid:2)
Revised: E π (cid:2)

Original: ∑ Tt = 1 r t (cid:3) .
Revised: ∑ Tt = 1 r t (cid:3) .

Original: Here T denotes the number of steps of interactions between the retriever and the reader.
Revised: Here T denotes the number of steps of interactions between the retriever and the reader.

Original: We treat the reward at each step equally and do not apply any discounting.
Revised: We treat the reward at each step equally and do not apply any discounting.

Original: Motivated by the R EINFORCE (Williams, 1992) algorithm, we compute the gradient of our objective as
Revised: Motivated by the R EINFORCE (Williams, 1992) algorithm, we compute the gradient of our objective as

Original: [Equation]
Revised: [Equation]

Original: Here, p t is the top-ranked paragraph returned by the retriever at time t and the probability that the current policy assigns to p t is computed by normalizing the scores with a softmax.
Revised: Here, p t is the top-ranked paragraph returned by the retriever at time t and the probability that the current policy assigns to p t is computed by normalizing the scores with a softmax.

Original: It is usual practice to add a variance reduction baseline (e.g. average of rewards in the minibatch) for stable training, but we found this signiﬁcantly degrades the ﬁnal performance.
Revised: It is usual practice to add a variance reduction baseline (e.g. average of rewards in the minibatch) for stable training, but we found this signiﬁcantly degrades the ﬁnal performance.

Original: We think this is because, in QA, a minibatch consists of questions of varying difﬁculty and hence the rewards in a batch itself have high variance.
Revised: We think this is because, in QA, a minibatch consists of questions of varying difﬁculty and hence the rewards in a batch itself have high variance.

Original: This is similar to ﬁndings by Shen et al.
Revised: This is similar to ﬁndings by Shen et al.

Original: (2017) for closed domain-QA (e.g. SQ U AD).
Revised: (2017) for closed domain-QA (e.g. S QUAD ).

Revised: Pretraining the multi-step-reasoner .

Original: 3. P UTTING IT ALL TOGETHER
Revised: We also ﬁnd that pre-training the multi-step-reasoner before ﬁne tuning with RL to be effective. We train to rank the similarity between the reformulated query vector and one of the distantly-supervised correct paragraph vectors higher than the score of a randomly sampled paragraph vector. Speciﬁcally, let q denote the query vector, p ∗ , p (cid:48) denote the paragraph representation of paragraphs containing the correct answer string and a random sampled paragraph respectively. We follow the Bayesian Personalized Ranking approach of Rendle et al.

Revised: (2009) and maximize log ( σ ( q (cid:62) p ∗ − q (cid:62) p (cid:48) )) .

Revised: The paragraph representations p ∗ , p (cid:48) are kept ﬁxed and gradient only ﬂows through q to the parameters of the multi-step-reasoner.

Revised: 3. P UTTING IT ALL TOGETHER

Original: Our open-domain architecture is summarized above in Algorithm 1.
Revised: Our open-domain architecture is summarized above in Algorithm 1.

Original: Given a large text corpora (as a list of paragraphs), the corresponding paragraph embeddings (which can be trained by the procedure in (§ 2.1)) and hyper-parameters (T , k ), our model M returns a text span a as answer.
Revised: Given a large text corpora (as a list of paragraphs), the corresponding paragraph embeddings (which can be trained by the procedure in (§ 2.1)) and hyper-parameters (T , k ), our model M returns a text span a as answer.

Original: The multi-step interaction between the retriever and reader can be best understood by the for loop in line 2 of algorithm 1.
Revised: The multi-step interaction between the retriever and reader can be best understood by the for loop in line 2 of algorithm 1.

Original: The initial query q 0 is ﬁrst used to rank all the paragraphs in the corpus (line 3), followed by which the top k paragraphs are sent to the reader (line 4).
Revised: The initial query q 0 is ﬁrst used to rank all the paragraphs in the corpus (line 3), followed by which the top k paragraphs are sent to the reader (line 4).

Original: The reader returns the answer span (with an associated score for the span) and also its internal state (line 5).
Revised: The reader returns the answer span (with an associated score for the span) and also its internal state (line 5).

Original: The GRU network then takes in the current query and the reader state to produce the updated query which is then passed to the retriever (line 6).
Revised: The GRU network then takes in the current query and the reader state to produce the updated query which is then passed to the retriever (line 6).

Original: The retriever uses this updated query to again re-rank the paragraphs and the entire process is repeated for T steps.
Revised: The retriever uses this updated query to again re-rank the paragraphs and the entire process is repeated for T steps.

Original: At the end of T steps, the model returns the span with the highest score returned by the reader model.
Revised: At the end of T steps, the model returns the span with the highest score returned by the reader model.

Original: The reader is trained using supervised learning (using the correct spans as supervision) and the parameters of the GRU network are trained using reinforcement learning.
Revised: The reader is trained using supervised learning (using the correct spans as supervision) and the parameters of the GRU network are trained using reinforcement learning.

Original: During training, we ﬁrst pre-train the reader model by setting the number of multi-step reasoning steps (T = 1).
Revised: During training, we ﬁrst pre-train the reader model by setting the number of multi-step reasoning steps (T = 1).

Original: After the training converges, we freeze the parameters of the reader model and train the parameters of the GRU network using policy gradients.
Revised: After the training converges, we freeze the parameters of the reader model and train the parameters of the GRU network using policy gradients.

Original: The output of the reader model is used to generate the reward which is used to train the policy network.
Revised: The output of the reader model is used to generate the reward which is used to train the policy network.

Original:  R ELATED W ORK
Revised:  R ELATED W ORK

Original: Open domain QA is a well-established task that dates back to few decades of reasearch.
Revised: Open domain QA is a well-established task that dates back to few decades of reasearch.

Original: For example the B ASEBALL system (Green Jr et al., 1961) aimed at answering open domain question albeit for a speciﬁc domain.
Revised: For example the B ASEBALL system (Green Jr et al., 1961) aimed at answering open domain question albeit for a speciﬁc domain.

Original: Open-domain QA has been popularized by the Trec-8 task (Voorhees et al., 1999).
Revised: Open-domain QA has been popularized by the Trec-8 task (Voorhees et al., 1999).

Original: Research has still progressed in open-domain settings with the introduction of datasets such as SearchQA Dunn et al.
Revised: Research has still progressed in open-domain settings with the introduction of datasets such as SearchQA Dunn et al.

Original: Quasar (Dhingra et al., 2017), TriviaQA (Joshi et al., 2017) but performance is still unsatisfactory.
Revised: Quasar (Dhingra et al., 2017), TriviaQA (Joshi et al., 2017) but performance is still unsatisfactory.

Original: In many open-domain systems (Chen et al., 2017; Clark & Gardner, 2018), the retriever is a simple IR based system (e.g. tﬁdf retriever) with no trainable parameters and hence the retriever cannot overcome from its mistakes.
Revised: In many open-domain systems (Chen et al., 2017; Clark & Gardner, 2018), the retriever is a simple IR based system (e.g. tﬁdf retriever) with no trainable parameters and hence the retriever cannot overcome from its mistakes.

Original: Recent work such as R 3 (Wang et al., 2018a) use a trained retriever and have shown improvement in performance.
Revised: Recent work such as R 3 (Wang et al., 2018a) use a trained retriever and have shown improvement in performance.

Original: However R 3 forms query dependent paragraph representation and such architectures will not scale to full open-domain settings where the retriever has to rank millions of paragraphs.
Revised: However R 3 forms query dependent paragraph representation and such architectures will not scale to full open-domain settings where the retriever has to rank millions of paragraphs.

Original: The retriever and reader of R 3 and DS-QA Lin et al.
Revised: The retriever and reader of R 3 and DS-QA Lin et al.

Original: (2018) are trained jointly but they do not support iterative reasoning thereby failing to recover from any mistakes made by the ranker or where evidence needs to be aggregated across multiple paragraphs.
Revised: (2018) are trained jointly but they do not support iterative reasoning thereby failing to recover from any mistakes made by the ranker or where evidence needs to be aggregated across multiple paragraphs.

Original: Lastly, we note that there has been work (Huang et al., 2013) to use deep neural network based models for better retrieval or web search but these model do not train a retriever and a QA model jointly.
Revised: Lastly, we note that there has been work (Huang et al., 2013) to use deep neural network based models for better retrieval or web search but these model do not train a retriever and a QA model jointly.

Original: Models that do iterative reasoning (Shen et al., 2017; Liu et al., 2017) i.e. read a single paragraph multiple times, have proven to be effective in closed domain settings (e.g. SQ U AD).
Revised: Models that do iterative reasoning (Shen et al., 2017; Liu et al., 2017) i.e. read a single paragraph multiple times, have proven to be effective in closed domain settings (e.g. S QUAD ).

Original: Our model can be seen as doing iterative reasoning but in a more realistic, open-domain setting.
Revised: Our model can be seen as doing iterative reasoning but in a more realistic, open-domain setting.

Original: In fact, our architecture reduces to these models when the set of evidence consists of only one paragraph and hence can be seen as a strict generalization of these models.
Revised: In fact, our architecture reduces to these models when the set of evidence consists of only one paragraph and hence can be seen as a strict generalization of these models.

Original: Nogueira & Cho (2017) use query reformulation to maximize just the recall of a IR system whereas we use query reformulation to maximize the performance of a complete QA system.
Revised: Nogueira & Cho (2017) use query reformulation to maximize just the recall of a IR system whereas we use query reformulation to maximize the performance of a complete QA system.

Original: Perhaps the most related to our work is Active
Revised: Perhaps the most related to our work is Active

Original: Question Answering (AQA)
Revised: Question Answering (AQA)

Original: (Buck et al., 2018), which use reformulation of the natural language query to improve the performance of a BiDAF reader model on S EARCH QA.
Revised: (Buck et al., 2018), which use reformulation of the natural language query to improve the performance of a BiDAF reader model on S EARCHQA .

Original: We directly compare with AQA and show that our model achieves signiﬁcantly better performance.
Revised: We directly compare with AQA and show that our model achieves signiﬁcantly better performance.

Original: At a very high level, our framework can be viewed as an instance of a memory network (Sukhbaatar et al., 2015) where the multi-step-reasoner can be seen as the controller update step of a memory network type of inference.
Revised: At a very high level, our framework can be viewed as an instance of a memory network (Sukhbaatar et al., 2015) where the multi-step-reasoner can be seen as the controller update step of a memory network type of inference.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: [Table]
Revised: [Table]

Original: We now present experiments to show the effectiveness of each component of our framework.
Revised: We now present experiments to show the effectiveness of each component of our framework.

Original: We experiment on the following large open-domain QA datasets — (a) T RIVIA QA-unﬁltered– a version of
Revised: We experiment on the following large open-domain QA datasets — (a) T RIVIAQA -unﬁltered– a version of

Original: T RIVIA QA (Joshi et al., 2017) built for open-domain
Revised: T RIVIAQA (Joshi et al., 2017) built for open-domain

Original: QA.
Revised: [Table] [Table] R 3 (Wang et al., 2018a) [Figure]

Revised: Our Retriever (initial)

Revised: [Figure]

Revised: [Figure]

Revised: + multi-step

Revised: [Figure]

Revised: steps)

Original:  It has much more number of paragraphs than the web/wiki setting of T RIVIA QA.
Revised: [Figure] [Figure] QA. It has much more number of paragraphs than the web/wiki setting of T RIVIAQA .

Original: Moreover, there is no guarantee that every document in the evidence will contain the answer making this setting more challenging.
Revised: Moreover, there is no guarantee that every document in the evidence will contain the answer making this setting more challenging.

Original: (b) T RIVIA QA-open– To test our framework for large scale setting, we combine all evidence for every question in the development set.
Revised: (b) T RIVIAQA -open– To test our framework for large scale setting, we combine all evidence for every question in the development set.

Original: This resulted in a corpus containing 1.68M paragraphs per question.
Revised: This resulted in a corpus containing 1.68M paragraphs per question.

Original: In other words, to answer a question, our model has to ﬁnd the correct answer from 1.68M paragraphs.

Original: (c) S EARCH QA (Dunn et al., 2017) – is another open-domain dataset which consists of question-answer pairs crawled from the J!
Revised: (c) S EARCHQA (Dunn et al., 2017) – is another open-domain dataset which consists of question-answer pairs crawled from the J!

Original: archive.
Revised: archive.

Original: The paragraphs are obtained from 50 web

Original: [Table]

Original: Model

Original: [Table]

Original: P@

Original: [Table]

Original: P@

Original: R 3 (Wang et al., 2018a)

Original: [Table]

Original: [Table]

Original: [Table]

Original: [Table]

Original: Our Retriever 35.

Original: [Table]

Original: + multi-step (7 steps)

Original: [Table]

Original: based retriever of R 3 is a more powerful model than our intial retrieval model.

Original: However, we ﬁnd that after doing few steps of iterative interaction between retriever and reader leads to better re-ranking and improved results.

Original: [Figure] [Table] [Figure] snippets retrieved using the Google search API.
Revised: The paragraphs are obtained fromweb snippets retrieved using the Google search API.

Original: (d) Q UASAR -T (Dhingra et al., 2017) – consists of 43K open-domain questions where the paragraphs are obtained from the ClueWeb data source.
Revised: (d) Q UASAR - T (Dhingra et al., 2017) – consists of 43K open-domain questions where the paragraphs are obtained from the ClueWeb data source.

Original: (e) SQ U AD-open– We also experimented on the open domain version of the SQ U AD dataset.
Revised: (e) S QUAD -open– We also experimented on the open domain version of the S QUAD dataset.

Original: For fair comparison to baselines, our evidence corpus was created by retrieving the top-5 wikipedia documents as returned by the pipeline of Chen et al.
Revised: For fair comparison to baselines, our evidence corpus was created by retrieving the top-5 wikipedia documents as returned by the pipeline of Chen et al.

Original: The datasets are summarized in table 1.
Revised: The datasets are summarized in table 1.

Original: 5. P ERFORMANCE OF P ARAGRAPH R ETRIEVER
Revised: 5. P ERFORMANCE OF P ARAGRAPH R ETRIEVER

Original: We ﬁrst investigate the performance of our paragraph retriever model (§2.1).
Revised: We ﬁrst investigate the performance of our paragraph retriever model (§2.1).

Original: Our retriever is based on inner product between the query and pre-computed paragraph vectors.
Revised: Our retriever is based on inner product between the query and pre-computed paragraph vectors.

Original: This architecture, although more scalable, is less powerful than the retrievers of R 3 and DS-QA which compute query-dependent passage representation via soft alignment (attention) before encoding with a bi-LSTM.
Revised: This architecture, although more scalable, is less powerful than the retrievers of R 3 and DS-QA which compute query-dependent passage representation via soft alignment (attention) before encoding with a bi-LSTM.

Original: Results in table 3 indeed show that retriever of R 3 has better P@ k than our retriever.
Revised: Results in table 3 indeed show that retriever of R 3 has better P@ k than our retriever.

Original: We also measure the performance of our retriever after few steps (#steps = 7) of interaction between the retriever and reader.
Revised: We also measure the performance of our retriever after few steps (#steps = 7) of interaction between the retriever and reader.

Original: As we can see from table 3, the query reformulation has resulted in better re-ranking and hence an overall improved performance of the retriever.
Revised: As we can see from table 3, the query reformulation has resulted in better re-ranking and hence an overall improved performance of the retriever.

Original: To test for scalability in open-domain settings, we conduct a synthetic experiment.
Revised: To test for scalability in open-domain settings, we conduct a synthetic experiment.

Original: We plot the wall-clock time (in seconds) taken by the retriever to score and rank paragraphs with a query.
Revised: We plot the wall-clock time (in seconds) taken by the retriever to score and rank paragraphs with a query.

Original: To test for scalability, we increase the number of paragraphs  from 500 to 100 million and test on a single Titan-X GPU.
Revised: To test for scalability, we increase the number of paragraphs ranging from 500 to 100 million and test on a single Titan-X GPU.

Original: For our baseline, we use the GPU implementation of the retriever of DS-QA (Lin et al., 2018).
Revised: For our baseline, we use the GPU implementation of the retriever of DS-QA (Lin et al., 2018).

Original: For our model, we test on three variants — (a) inner product on GPU, (b) inner-product on CPU and (c) inner-product using SG-Tree.
Revised: For our model, we test on three variants — (a) inner product on GPU, (b) inner-product on CPU and (c) inner-product using SG-Tree.

Original: Figure 2 shows the results.
Revised: Figure 2 shows the results.

Original: Our retriever model on GPU is faster than DS-QA, since the latter perform much more computation compared to just inner-product in our case.
Revised: Our retriever model on GPU is faster than DS-QA, since the latter perform much more computation compared to just inner-product in our case.

Original: Moreover, DS-QA quickly uses up all the available memory and throws a memory error (OOM) by just 100K paragraphs.
Revised: Moreover, DS-QA quickly uses up all the available memory and throws a memory error (OOM) by just 100K paragraphs.

Original: The inner product operation scales up to 1M paragraph before going OOM.
Revised: The inner product operation scales up to 1M paragraph before going OOM.

Original: Our nearest neighbor approach (SG-Tree) shows very impressive performance even though it operates on CPU and consistently outperforms dot product operation on the CPU.
Revised: SG-Tree shows very impressive performance even though it operates on CPU and consistently outperforms dot product operation on the CPU.

Original: 5. E FFECTIVENESS OF MULTI - STEP - REASONER
Revised: 5. E FFECTIVENESS OF MULTI - STEP - REASONER

Original: Next we investigate theoverall improvements inthe QA performance because of the iterative interaction between the retriever and the reader.
Revised: Next we investigate the improvements in QA performance due to multi-step-reasoner. We report the commonly used exact-match (EM) and F1 score on each dataset.

Original: Performance on benchmark datasets.
Revised: Performance on benchmark datasets.

Original: Table 2 compares the performance of our model with various competitive baselines on four open-domain QA datasets.
Revised: Table 2 compares the performance of our model with various competitive baselines on four open-domain QA datasets.

Original: The key highlights of the results in this table are (a) We perform comparably well on all the datasets and to the best of our knowledge, our model achieves the highest reported number on the hidden test set of T RIVIA QA-unﬁltered 2 .

Original: (b) Our framework achieves uniform boost in scores on two popular and widely used machine reading architectures — Dr.QA  (Chen et al., 2017) and BiDAF (Seo et al., 2017).
Revised: One of the main observation is that combining multi-step-reasoner with the base Dr.QA reader (Chen et al., 2017) always leads to improved performance.

Original: This show that our model is indeed agnostic to the neural architecture of the machine reading model provided it has access to the token level hidden representations of the reader. We are waiting on the ofﬁcial results of BiDAF on the hidden test set of T RIVIA QA-unﬁltered but we are hopeful that the trend will remain the same.
Revised: We also perform competitively to most baselines across all datasets.

Original: [Figure]
Revised: [Figure]

Original: Performance w.r.t varying number of steps.

Original: Figure 3 shows the relative improvements our models achieve on Q UASAR -T, S EARCH QA and
Revised: Figure 3 shows the relative improvements our models achieve on Q UASAR - T , S EARCHQA and

Original: T RIVIA QA-unﬁltered with varying number of steps of interaction between the retriever and the reader.
Revised: T RIVIAQA -unﬁltered with varying number of steps of interaction between the retriever and the reader.

Original: The key takeaway of this experiment is that multiple steps of interaction uniformly increases performance over base model (with no interaction).
Revised: The key takeaway of this experiment is that multiple steps of interaction uniformly increases performance over base model (with no interaction).

Original: Different datasets have varying level of difﬁculties, however, the performance reaches its peak around 5 to 7 steps and does not provide much beneﬁt on further increasing the number of steps.
Revised: Different datasets have varying level of difﬁculties, however, the performance reaches its peak around 5 to 7 steps and does not provide much beneﬁt on further increasing the number of steps.

Original: Large scale experiment on T RIVIA QA-open .
Revised: Large scale experiment on T RIVIAQA -open .

Original: The benchmark datasets for open-domain QA have on an average hundred paragraphs for each question (Table 1).
Revised: The benchmark datasets for open-domain QA have on an average hundred paragraphs for each question (Table 1).

Original: In real open-domain settings, the size of the evidence corpus might be much bigger.
Revised: In real open-domain settings, the size of the evidence corpus might be much bigger.

Original: To test our model for the same, we created the
Revised: To test our model for the same, we created the

Original: T RIVIA QA-open setting, in which for each query we combined all the paragraphs in the development set, resulting in 1.6M paragraphs per query.
Revised: T RIVIAQA -open setting, in which for each query we combined all the paragraphs in the development set, resulting in 1.6M paragraphs per query.

Original: We want to emphasize that the baseline models such as
Revised: We want to emphasize that the baseline models such as

Original: R 3 and DS-QA will not scale to this large setting.
Revised: R 3 and DS-QA will not scale to this large setting.

Original: The baseline DrQA.
Revised: The baseline DrQA.

Original: model with no interaction between the retriever and reader get a score of EM = 37.45 and F1 =
Revised: model with no interaction between the retriever and reader get a score of EM = 37.45 and F1 =

Original: The same model withsteps of interaction achieves a score of EM = 39.76 and F1 =
Revised: The same model withsteps of interaction achieves a score of EM = 39.76 and F1 =

Original: The key takeaways from this experiment are — (a) our framework can scale to settings containing millions of paragraphs, (b) iterative interaction still increase performance even in large scale settings, (c) the overall performance has signiﬁcantly decreased (from 61 to 44.3 F1), suggesting that large context in open-domain QA is very challenging and should be an active area of research with signiﬁcant scope for improvement.
Revised: The key takeaways from this experiment are — (a) our framework can scale to settings containing millions of paragraphs, (b) iterative interaction still increase performance even in large scale settings, (c) the overall performance has signiﬁcantly decreased (from 61 to 44.3 F1), suggesting that large context in open-domain QA is very challenging and should be an active area of research with signiﬁcant scope for improvement.

Original: [Table]
Revised: [Table]

Original: 5. A NALYSIS OF R ESULTS
Revised: 5. A NALYSIS OF R ESULTS

Original: This section does further analysis of the results of the model.
Revised: This section does further analysis of the results of the model.

Original: Speciﬁcally we are interested in analyzing if our method is able to gather more relevant evidence as we increase the number of steps of interaction between the retriever and the reader.
Revised: Speciﬁcally we are interested in analyzing if our method is able to gather more relevant evidence as we increase the number of steps of interaction between the retriever and the reader.

Original: We conduct this analysis on the development set of S EARCH QA (containing 13,393 queries) and to simplify analysis, we make the retriever choose the top scored single paragraph to send to the reader, at each step.
Revised: We conduct this analysis on the development set of S EARCHQA (containing 13,393 queries) and to simplify analysis, we make the retriever choose the top scored single paragraph to send to the reader, at each step.

Original: The results are noted in table 4.
Revised: The results are noted in table 4.

Original: As we can see from row 1 of table 4, on increasing steps of interaction, the quality of retrieved paragraphs becomes better, i.e. even though the correct paragraph (containing the answer string) was not retrieved in the ﬁrst step, the retriever is able to fetch relevant paragraphs later.
Revised: As we can see from row 1 of table 4, on increasing steps of interaction, the quality of retrieved paragraphs becomes better, i.e. even though the correct paragraph (containing the answer string) was not retrieved in the ﬁrst step, the retriever is able to fetch relevant paragraphs later.

Original: It is also unsurprising to see, that when correct paragraphs are retrieved, the performance of the reader also increases.
Revised: It is also unsurprising to see, that when correct paragraphs are retrieved, the performance of the reader also increases.

Original: To check if the our policy was exploring and retrieving different paragraphs, we counted the mean number of unique paragraphs retrieved across all steps and found that it is high (around 2) when #steps of interaction is small as 3, suggesting that the policy chooses to explore initially.
Revised: To check if the our policy was exploring and retrieving different paragraphs, we counted the mean number of unique paragraphs retrieved across all steps and found that it is high (around 2) when #steps of interaction is small as 3, suggesting that the policy chooses to explore initially.

Original: As the number of steps is increased, it increases at slower rate suggesting that if the policy has already found good paragraphs for the reader, it chooses to exploit them rather than choosing to explore.
Revised: As the number of steps is increased, it increases at slower rate suggesting that if the policy has already found good paragraphs for the reader, it chooses to exploit them rather than choosing to explore.

Original: Figure 4 shows two instances where iterative interaction is helpful.
Revised: Figure 4 shows two instances where iterative interaction is helpful.

Original: In ﬁgure to the left, the retriever is initially unable to ﬁnd a paragraph that directly answers the question, however it ﬁnds a paragraph which gives a different name for the disease allowing it to ﬁnd a more relevant paragraph that directly answers the query.
Revised: In ﬁgure to the left, the retriever is initially unable to ﬁnd a paragraph that directly answers the question, however it ﬁnds a paragraph which gives a different name for the disease allowing it to ﬁnd a more relevant paragraph that directly answers the query.

Original: In the ﬁgure to the right, after the query reformulation, both the retrieved paragraphs have the correct answer string.
Revised: In the ﬁgure to the right, after the query reformulation, both the retrieved paragraphs have the correct answer string.

Original: Since we aggregate (sum) the scores of spans, this leads to an increase in the score of the right answer span (Demeter) to be the maximum.
Revised: Since we aggregate (sum) the scores of spans, this leads to an increase in the score of the right answer span (Demeter) to be the maximum.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this paper, we introduce a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other.
Revised: In this paper, we introduce a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other.

Original: The resulting framework improved accuracy in question answering task by 9.5% as compared to previous results establishing new state-of-the-art results on the challenging TriviaQA-unﬁltered dataset.
Revised: The resulting framework improved accuracy in question answering task by 9.5% as compared to previous results establishing new state-of-the-art results on the challenging TriviaQA-unﬁltered dataset.

Original: Our method also performs competitively on three other open-domain datasets namely — S EARCH QA, Q UASAR -T and SQ U ADopen.
Revised: Our method also performs competitively on three other open-domain datasets namely — S EARCHQA , Q UASAR - T and S QUADopen.

Original: We also show that our fast retrieval method can scale upto tens of millions of paragraph, much beyond the current capability of existing open-domain systems with a trained retriever module.
Revised: We also show that our fast retrieval method can scale upto tens of millions of paragraph, much beyond the current capability of existing open-domain systems with a trained retriever module.

Original: We also demonstrate that our multi-step interaction brings uniform increase in performance on all datasets.
Revised: We also demonstrate that our multi-step interaction brings uniform increase in performance on all datasets.

Original: Finally, we show that our method is agnostic to the architecture of the machine reading system provided we have access to the token level hidden representations of the reader.
Revised: Finally, we show that our method is agnostic to the architecture of the machine reading system provided we have access to the token level hidden representations of the reader.

Original: Our method brings an increase in performance to two popular and widely used neural machine reading architectures —
Revised: Our method brings an increase in performance to two popular and widely used neural machine reading architectures —

Original: Dr.QA and BiDAF.
Revised: Dr.QA and BiDAF.