Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering

The open-domain question answering ( OpenQA ) task aims to extract answers that match specific questions from a distantly supervised corpus . Unlike supervised reading comprehension ( RC ) datasets where questions are designed for particular paragraphs , background sentences in OpenQA datasets are more prone to noise . We observe that most existing OpenQA approaches are vulnerable to noise since they simply regard those sentences that contain the answer span as ground truths and ignore the plausible correlation between the sentences and the question . To address this deficiency , we introduce a unified and collaborative model that leverages alignment information from query-sentence pairs in a small-scale supervised RC dataset and aggregates relevant evidence from distantly supervised corpus to answer open-domain questions . We evaluate our model on several real-world OpenQA datasets , and experimental results show that our collaborative learning methods outperform the existing baselines significantly . 1 INTRODUCTION . Driven by neural networks with attention mechanisms and large-scale supervised reading comprehension ( RC ) datasets , teaching the machine to read within close-domain paragraphs has grown in leaps and bounds ( Chen et al. , 2016 ; Dhingra et al. , 2017a ; Cui et al. , 2017 ; Shen et al. , 2017 ; Wang et al. , 2017 ) . Recently , a new challenge called open-domain question answering ( OpenQA ) arises with the publishment of several distantly supervised OpenQA datasets . Different from supervised RC datasets where designed questions are oriented to particular paragraphs , background sentences in OpenQA datasets are usually retrieved by information retrieval systems using simple matching tactics , which leads to the noise-prone nature of this task . Existing approaches for OpenQA can be roughly divided into the aggregation-based methods ( Wang et al. , 2018b ; Lin et al. , 2016 ; Clark & Gardner , 2018 ) that aggregate all possible evidence to predict the answer and the selection-based methods ( Choi et al. , 2017 ; Wang et al. , 2018a ; Min et al. , 2018 ) that only select a minimal set of sentences for question answering . These models simply follow the distantly supervised assumption , i.e. , regarding all the sentences that contain the answer span as ground truths . However , in many cases , plausible correspondence may not exist between the question and those sentences . For example , given the question “ Which country has the fourth largest population ? ” , the assumption of distant supervision regards both of the two retrieved sentences to be valid : ( 1 ) “ With well over 210 million people , Indonesia is the fourth most populous country in the world. ” ( 2 ) “ ... , Indonesia is New Zealand ’ s fourth largest source of imports. ” In fact , instead of treating those candidate sentences equally , it makes more sense to give different semantic labels to those sentences based on their plausible correlation with the question . For the above example , the first sentence is supposed to have a higher semantic label than the second since it precisely answers the given question while the second fails . In this paper , we propose an aggregation-based OpenQA model consisting of a sentence discriminator which explicitly computes the query-sentence matching score and a sentence reader that selects a proper answer span from the given sentence . We show that by explicitly mearsuring the relatedness between the sentence and the question , the model could effectively leverage structure information at the sentence level to promote the overall performance of question answering . Moreover , to address the problem of lacking supervised signal in distantly supervised OpenQA datasets , we learn a semantic labeler by incorporating the query-sentence alignment information from a small-scale set of supervised reading comprehension . We propose two denoising strategies including semi-supervised learning with semantic labels and collaborative learning that further encourages cross-domain information sharing . We evaluate our work on three large-scale OpenQA datasets including Quasart-T ( Dhingra et al. , 2017b ) , SearchQA ( Dunn et al. , 2017 ) and the unfiltered version of TriviaQA ( Joshi et al. , 2017 ) . The experiment results show that our model achieves significant improvement as compared to the published baselines on these datasets . Besides , we show that via collaboratively learning with the semantic labeler , our sentence discriminator is capable of distinguishing relevant sentences from those irrelevant ones , and significantly promoting the answer prediction process in the overall pipeline . 2 METHODOLOGY . 2.1 TASK FORMULATION . The OpenQA task aims to extract the answer of a given question from a set of distantly supervised texts . Formally , given a question with m words Q = { q1 , q2 . . . , qm } and a set of n distantly supervised background sentences C = { D1 , · · · , Di , · · · , Dn } , the task is to extract an answer span from the background sentence set C . 2.2 MODEL ARCHITECTURE . As illustrated in Figure 1 , the high-level structure of our model is comprised of a semantic labeler and an aggregation-based OpenQA model . The aggregation-based OpenQA model handles the question-answering task on the distantsupervised dataset . During training , we maximize the probability of the answer spanA being correct given the question Q by aggregating probabilities in various paragraphs . The semantic labeler is learned from the supervised reading comprehension dataset S = { ( QS , CS , AS , yS ) , . . . } to capture valid query-sentence correlation . Then , it is used to produce soft labels ŷT for query-sentence pairs in the distant-supervised dataset T = { ( QT , CT , AT ) , . . . } where accurate sentence labels are absent . In the following sections , we first describe our OpenQA model in details and then demonstrate our two denoising strategies . 2.3 AGGREGATION-BASED OPENQA MODEL . Our aggregation-based OpenQA model consists of a sentence discriminator and a sentence reader . For both of the two sub-models , we use a generic encoder to construct representations for the question and the sentence . The decoders of the two sub-models , however , are more task-specific . 2.3.1 SENTENCE DISCRIMINATOR . The sentence discriminator is divided into the encoder part and the sentence-level decoder part , which will be illustrated in the following respectively . Encoder The encoder takes a sentence and question as input and yields the respective representations . Without loss of generality , we adopt an encoder similar to Chen et al . ( 2017 ) in structure which involves an embedding layer , a context-query interactive layer and an encoder layer . We denote the word embeddings as Demb ∈ Rd×l for the sentence and Qemb ∈ Rd×m for the question . First , the embeddings are fed into the context-query interactive layer to obtain the interactive embedding of the sentence : S = f ( Demb ) T f ( Qemb ) ∈ Rl×m , Ai : = exp ( Si : ) ∑l k=1 , exp ( Sk : ) ∈ Rm , Dq = QembAT ∈ Rh×l , ( 1 ) where we use ReLU as our nonlinear function f ( · ) . Then , we encode the embeddings using bidirectional LSTM to capture context-aware representations . Denc = BiLSTM ( [ Demb , Dq ] ) , Qenc = BiLSTM ( Qemb ) . ( 2 ) Sentence-level Decoder The sentence-level decoder computes a score for the sentence given Denc and Qenc . We first merge the encoded question representation to obtain a fix length hidden vector with self-attention weights : α = softmax ( wT1Q enc ) ∈ Rm , q̃enc = m∑ i=1 ( αiQ enc i : ) ∈ Rh , ( 3 ) where w1 ∈ Rh is a trainable weight vector . Next , we calculate bilinear similarity to construct a question-aligned sentence representation Dalign , which is further used to calculate the sentence score . Dalign = q̃encW2D enc ∈ Rh ′×l , ( 4 ) score = softmax ( W3 max i ( Dalign : i ) ) . ( 5 ) where W2 ∈ Rh×h ′×h , W3 ∈ R2×h ′ are trainable parameters . Note that h′ is a hyperparameter varied between different models . 2.3.2 SENTENCE READER . The sentence reader consists of the same encoder as the sentence discriminator and a token-level decoder that computes the probability of the given text span being the answer . Token-level Decoder First , we merge question representations using ( 3 ) and obtain q̃enc , which is later used to compute the probabilities of each token being the two ends of the span as given by ( 6 ) . The answer probability is obtained by multiplying the probabilities for the start token Ds and the end token De . Ps ( Dj ) = softmax ( q̃encW4D enc : j ) , Pe ( Dj ) = softmax ( q̃encW5D enc : j ) , ( 6 ) P ( A|D , Q ) = Ps ( Ds ) × Pe ( De ) . ( 7 ) Here , W4 ∈ Rh×h , W5 ∈ Rh×h are learnable parameters during training . 2.3.3 TRAINING AND PREDICTION . We train the OpenQA model in two stages . Firstly , the sentence discriminator is trained on the OpenQA dataset to produce a score Ri for each sentence in the set { Di } ni=1 . We give each sentence a distantly supervised label ỹ = { 0 , 1 } indicating whether the sentence contains a text span that matches the answer exactly and optimize the cross-entropy loss . LD = 1 n n∑ i=1 −ỹi log ( Ri ) − ( 1− ỹi ) log ( 1−Ri ) . ( 8 ) Then , in the second stage , we fix the sentence disctiminator and train the sentence reader to optimize the overall probability of the answer P ( A|Q ) . We normalize each sentence score Ri to approximate the distribution P ( Di|Q ) , which is formally calculated as : P ( Di|Q ) = Ri∑n j=1Rj , P ( A|Q ) = n∑ i=1 P ( A|Di , Q ) P ( Di|Q ) . ( 9 ) And the overall loss of the aggregation-based OpenQA model is defined as the negative sum of log probabilities : LQA = − 1 |T | ∑ ( A , Q ) ∈T logP ( A|Q ) . ( 10 ) When predicting the answer , we first decode top k answer spans { Aj } kj=1 for each sentence Di based on P ( Aj |Di , Q ) and build a candidate set with k × n candidates . Then , for each candidate , we calculate the overall probability P ( Aj |Q ) by considering its total occurrences in the background sentences given by ( 9 ) . Finally , we select the candidate with the maximimal overall probability . 2.4 DENOISING STRATEGIES ON OPENQA . Our denoising strategies on the OpenQA task combine the aggregated OpenQA model and the semantic labeler . In the following , we first introduce our semantic labeler and then two denoising models respectively . 2.4.1 SEMANTIC LABELER . The semantic labeler aims to transfer knowledge from the supervised dataset S = { ( QS , DS , AS , yS ) , . . . } which we refer to as the source domain , to the distantly supervised datasets T = { ( QT , DT , AT ) , . . . } which we refer to as target domains . To this end , we employ a structure similar to the sentence discriminator . For multi-task requirement , we construct the embedding layer with both domain ’ s vocabularies to encourage the model to learn unified representations for words that lie within the intersection of both domain ’ s vocabularies . As for the decoder , we use the sentence-level decoder with h′ = 1 for better scalability on the distant-supervised dataset . This setting improves the generalization of the semantic labeler and prevents it from overfitting on the small-scale source domain during training .

Summary: This paper aims for open-domain question answering with distant supervision. First, the authors proposed an aggregation-based openQA model with sentence discriminator and sentence reader. Second, they use a semantic labeler to handle distant supervision problem by utilizing other span supervision tasks, and propose two different denoising methods. They run experiments on 3 open-domain QA datasets and achieve SOTA.