MULTI-SPAN QUESTION ANSWERING USING SPAN-IMAGE NETWORK

1 INTRODUCTION . Answering questions posted as text to search engines or spoken to virtual assistants like Alexa has become a key feature in information retrieval systems . Publicly available reading comprehension datasets including WikiQA ( Yang et al. , 2015 ) , TriviaQA ( Joshi et al. , 2017 ) , NewsQA ( Trischler et al. , 2016 ) , and SQuAD ( Rajpurkar et al. , 2016 ) have fostered research in QA models . SQuAD is one of the most widely-used reading comprehension benchmarks that has an active leaderboard with many participants . Even though there are models that beat human-level accuracy in SQuAD , these QA systems can do well by learning only context and type-matching heuristics ( Weissenborn et al. , 2017 ) but may still be far from true language understanding since they do not offer robustness to adversarial sentences ( Jia & Liang , 2017 ) . To better measure performance , SQuAD v2.0 Rajpurkar et al . ( 2018 ) extends v1.1 by allowing questions that have no explicit answers in a given paragraph . QA can be modeled as a task to predict the span ( i.e. , start and end indices ) of an answer given a question and an input paragraph . To find the answer span , language representation models such as BERT can be used to associate a question with a given paragraph Devlin et al . ( 2019 ) . BERT is pre-trained on unsupervised tasks using large corpora . Its input representation permits a pair , which is well suited for having a question and a passage as input . By fine-tuning BERT on SQuAD , a QA model can be obtained . Questions without an answer are treated as having a span that begins and ends with the special BERT token : [ CLS ] . In this way , a BERT-based QA model can offer an actual answer or ‘ no-answer ” to all questions in SQuAD v1.1 and v2.0 datasets . Prior work on QA assumes presence of a single answer or lack of any answer Seo et al . ( 2016 ) , Devlin et al . ( 2019 ) . Furthermore , they assume a separable probability distribution function ( pdf ) for start and end indices of an answer span , which leads to a separable loss function . This approach has two major disadvantages : 1 ) It prevents the QA model from predicting multiple spans without postprocessing . 2 ) Since a separable pdf is used , the QA model can not learn to evaluate compatibility of start and end indices , thus suffering from performance degradation . Pang et al . ( 2019 ) consider a hierarchical answer span by sorting the product of start and end probabilities to support multiple spans . However , they still assume a separable pdf for start and end indices . To the best of our knowledge , a multi-span QA architecture has not been proposed . We introduce Span-Image architecture to enable multi-span answers ( or multiple answers ) given a question and a paragraph . Each pixel ( i , j ) in the span-image corresponds to a span starting at ith position and ending at jth . Typical image processing networks like 2D convolutional network layers are used . Span-Image architecture enables the model to couple start and end indices to check for their compatibility . Constraints such as “ the end index has to be bigger than the start index ” , can be automatically embedded into the model . Moreover , other span characteristics such as “ shorter answers are more likely to occur ” ( see Figure 1 ) , can be learned by the model , thus eliminating the need for post-processing or regularization . Our contributions are summarized as below : • We present Span-Image architecture , a novel method that enables multi-span answer prediction . • Specially designed image channels in the proposed architecture can help the QA model capture span-characteristics and eliminate the need for post-processing . • Span-Image network is modular and can be added to most DNN-based QA models without requiring changes to the previous layers . 2 MULTI-SPAN PREDICTION . In this section we first highlight BERT QA architecture , and present our span-image architecture that consumes BERT outputs . 2.1 BERT QA ARCHITECTURE . The QA task in BERT uses a separable pdf : p ( sS , sE ) = p ( sS ) × p ( sE ) where sS and sE denotes one-hot variables of length N for start and end indices for a paragraph of length N , respectively . Therefore , BERT QA architecture assumes start and end index probabilities to be independent from each other . Given predicted probabilities pBERT ( sS ) and pBERT ( sE ) as outputs of BERT , a question q of length M and a passage g of length N , the QA loss fuction for fine-tuning BERT is then given by Loss ( q , p , tS , tE ) = H ( tS , pBERT ( s S ) ) +H ( tE , pBERT ( s E ) ) , ( 1 ) where H is the cross-entropy function , and t is target span with start and end indices tS and tE , respectively . BERT has two separate outputs for start and end indices , which makes it impossible for the model to check for compatibility of sS and sE1 or utilize information such as span length ( i.e. , sE − sS ) in its predictions . Figure 2 shows BERT QA architecture . 1One can claim attention heads in the transformer network will correlate tokens but this does not happen explicitly as in our architecture , where probability for each possible span is computed jointly . 2.2 SPAN-IMAGE NETWORK . Span-image network does not dictate a separable pdf for start and end indices , hence p ( sS , sE ) 6= p ( sS ) × p ( sE ) . Given a question q and paragraph g , BERT outputs D dimensional vector sequence BERT ( q , g ) of length M +N + 2 ( see Figure 2 ) . Let ’ s denote the last N vectors in the sequence , which corresponds to paragraph g , with BERT g ( q , g ) . Using two affine transformations denoted by WS and WE , each of which has D units , we create 2 vector sequences WS ( BERT g ( q , g ) ) and WE ( BERT g ( q , g ) ) of length N . A pixel at location ( i , j ) has D channels and is given by span imi , j =W S ( BERT g ( q , g ) ) i ◦WE ( BERT g ( q , g ) ) j , ( 2 ) where ◦ denotes element-wise multiplication of D-dimensional vectors in ith and jth locations of WS ( BERT g ( q , g ) ) and WE ( BERT g ( q , g ) ) , respectively . Hence , span-image span im shown in Figure 3 , is a 3-dimensional tensor of depth D and of height and width N . This enables us to borrow techniques such as 2-dimensional convolutional filtering , max-pooling , and ReLU from convolutional neural network ( CNN ) architectures for image classification . The output of the spanimage network is an N × N logit-image , logit im , with a single channel ( i.e. , a logit for each possible pixel/span ) . Each channel in span im is a matrix of rank 1 . Therefore , each channel is separable and has limited potential beyond the separable approach described in Section 2.1 . However , applying twodimensional convolutional layers improves performance and makes logit im non-separable , thus eliminating the independence assumption on start and end indices . The probability of each span can be computed by applying sigmoid function on each pixel or softmax in logit im . Using sigmoid makes no assumption on number of spans , while using softmax assumes a single span in every paragraph . The best function to use depends on the QA dataset . For example , in our experiments , using softmax gave us best results for fine-tuning BERT on SQuAD while sigmoid performed better on our internal multi-span datasets . Denoting p ( sS = i , jsE = j ) by pi , j for simplicity , span probabilities for single-span and multi-span datasets can be computed by psigmoidi , j = sigmoid ( logit imi , j ) , if training datasets can have multi-span answers psoftmaxi , j = softmax ( logit im ) i , j , if training datasets only have single-span answers ( 3 ) Target image , target im , is a binary image with zeros at every pixel except those corresponding to target spans . ( i.e. , target im ( i , j ) = 1 for any target span in g with start index i and end index j ) . Given logit im and target im , the loss function using sigmoid is given below Loss ( q , p , target im ) = ∑ i , j H ( target imi , j , p sigmoid i , j ) /N2 , ( 4 ) where H is the cross-entropy function . The loss function for softmax is given by Loss ( q , p , target im ) = H ( target im , psoftmax ) . ( 5 ) Note that target im and psoftmax are joint pdfs on sS and sE , while psigmoidi , j is a pdf for binary variable indicating if ( i , j ) is an answer span or not . 2.2.1 INCORPORATING SPAN CHARACTERISTICS . Typical post-processing on BERT QA output involves checking for valid spans and sorting them with the multiplication of start/end index probabilities . As shown in Figure 1 , a priori information about the span length distribution can be used to break ties or prefer between spans that have close probabilities . In BERT QA , this can only be achieved by implementing a post-processing technique that penalizes spans based on their lengths . Our span-image architecture , however , has an inherent capability to learn and incorporate such patterns . We simply create a new channel span ch , and our model learns how to utilize this channel to capture span-characteristics during training . span ch = { −1 if i < j j − i if j ≥ i , j − i < ς ς if j − i > ς . ( 6 ) span ch is concatenated to span im increasing its depth to D + 1 . 3 EXPERIMENTS . 3.1 IMPLEMENTATION DETAILS . In multi-span QA tasks , we compare the standard BERT-QA model by Devlin et al . ( 2019 ) , which has a separate output layer for start and end index prediction , against the following variants of the span-image architecture , which is described in Section 2.2 : • bert-qa : The BERT base model that is available from Transformers ( Wolf et al. , 2019 ) library as bert-base-uncased . • bert-ms-sigmoid : Consists of the BERT base model augmented by the span-image network , which involves two 2D convolution layers with 100 and 50 filters , respectively . Both layers use 3x3 filters . The output layer has sigmoid activation on span-image pixels to enable multiple span predictions . • base-ms-softmax : Replaces the sigmoid activation of the bert-ms-sigmoid with softmax activation . Softmax serves as a useful regularization when task dictates one answer ( noanswer counts as a “ null ” answer ) . • bert-ms-sigmoid-sl : bert-ms-sigmoid model with a span-length indicator channel concatenated to the span-image . • bert-ms-softmax-sl : bert-ms-softmax model with a span-length indicator channel concatenated to the span-image .

Summary: The paper proposes a novel method for predicting multiple answer spans in question-answering (QA) tasks. When the Span-Image technique is applied to a base BERT model, the authors show performance gains on a single-span dataset (SQuAD) and substantial improvements on a multi-span dataset (an internal Amazon dataset). The authors propose that the method is both model-agnostic and can eliminate common post-processing steps via built-in architecture design choices.