Question answering (QA) has drawn a lot of attention in the past few years. QA tasks on images BIBREF0 have been widely studied, but most focused on understanding text documents BIBREF1 . A representative dataset in text QA is SQuAD BIBREF1 , in which several end-to-end neural models have accomplished promising performance BIBREF2 . Although there is a significant progress in machine comprehension (MC) on text documents, MC on spoken content is a much less investigated field. In spoken question answering (SQA), after transcribing spoken content into text by automatic speech recognition (ASR), typical approaches use information retrieval (IR) techniques BIBREF3 to find the proper answer from the ASR hypotheses. One attempt towards QA of spoken content is TOEFL listening comprehension by machine BIBREF4 . TOEFL is an English examination that tests the knowledge and skills of academic English for English learners whose native languages are not English. Another SQA corpus is Spoken-SQuAD BIBREF5 , which is automatically generated from SQuAD dataset through Google Text-to-Speech (TTS) system. Recently ODSQA, a SQA corpus recorded by real speakers, is released BIBREF6 .To mitigate the impact of speech recognition errors, using sub-word units is a popular approach for speech-related downstream tasks. It has been applied to spoken document retrieval BIBREF7 and spoken term detection BIBREF8 The prior work showed that, using phonectic sub-word units brought improvements for both Spoken-SQuAD and ODSQA BIBREF5 .Instead of considering sub-word features, this paper proposes a novel approach to mitigate the impact of ASR errors. We consider reference transcriptions and ASR hypotheses as two domains, and adapt the source domain data (reference transcriptions) to the target domain data (ASR hypotheses) by projecting these two domains in the shared common space. Therefore, it can effectively benefit the SQA model by improving the robustness to ASR errors in the SQA model.Domain adaptation has been successfully applied on computer vision BIBREF9 and speech recognition BIBREF10 . It is also widely studied on NLP tasks such as sequence tagging and parsing BIBREF11 , BIBREF12 , BIBREF13 . Recently, adversarial domain adaptation has already been explored on spoken language understanding (SLU). Liu and Lane learned domain-general features to benefit from multiple dialogue datasets BIBREF14 ; Zhu et al. learned to transfer the model from the transcripts side to the ASR hypotheses side BIBREF15 ; Lan et al. constructed a shared space for slot tagging and language model BIBREF16 . This paper extends the capability of adversarial domain adaptation for SQA, which has not been explored yet.In SQA, each sample is a triple, INLINEFORM0 , where INLINEFORM1 is a question in either spoken or text form, INLINEFORM2 is a multi-sentence spoken-form document, and INLINEFORM3 is the answer in text from. The task of this work is extractive SQA; that means INLINEFORM4 is a word span from the reference transcription of INLINEFORM5 . An overview framework of SQA is shown in Figure FIGREF1 . In this paper, we frame the source domain as reference transcriptions and the target domain as ASR hypotheses. Hence, we can collect source domain data more easily, and adapt the model to the target domain.In this task, when the machine is given a spoken document, it needs to find the answer of a question from the spoken document. SQA can be solved by the concatenation of an ASR module and a question answering module. Given the ASR hypotheses of a spoken document and a question, the question answering module can output a text answer.The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer. We used the standard evaluation script from SQuAD BIBREF1 to evaluate the performance.The used architecture of the QA model is briefly summarized below. Here we choose QANet BIBREF2 as the base model due to the following reasons: 1) it achieves the second best performance on SQuAD, and 2) since there are completely no recurrent networks in QANet, its training speed is 5x faster than BiDAF BIBREF17 when reaching the same performance on SQuAD.The network architecture is illustrated in Figure FIGREF2 . The left blocks and the right blocks form two QANets, each of which takes a document and a question as the input and outputs an answer. In QANet, firstly, an embedding encoder obtains word and character embeddings for each word in INLINEFORM0 or INLINEFORM1 and then models the temporal interactions between words and refines word vectors to contextualized word representations. All encoder blocks used in QANet are composed exclusively of depth-wise separable convolutions and self-attention. The intuition here is that convolution components can model local interactions and self-attention components focus on modeling global interactions. The context-query attention layer generates the question-document similarity matrix and computes the question-aware vector representations of the context words. After that, a model encoder layer containing seven encoder blocks captures the interactions among the context words conditioned on the question. Finally, the output layer predicts a start position and an end position in the document to extract the answer span from the document.The main focus of this paper is to apply domain adaptation for SQA. In this approach, we have two SQA models (QANets), one trained from target domain data (ASR hypotheses) and another trained from source domain data (reference transcriptions). Because the two domains share common information, some layers in these two models can be tied in order to model the shared features. Hence, we can choose whether each layer in the QA model should be shared. Tying the weights between the source layer and the target layer in order to learn a symmetric mapping is to project both source and target domain data to a shared common space. Different combinations will be investigated in our experiments.More specifically, we incorporate a domain discriminator into the SQA model shown in Figure FIGREF2 , which can enforce the embedding encoder to project the sentences from both source and target domains into a shared common space and consequentially to be ASR-error robust. Although the embedding encoder for both domains may implicitly learn some common latent representations, adversarial learning can provide a more direct training signal for aligning the output distribution of the embedding encoder from both domains. The embedding encoder takes in a sequence of word vectors and generates a sequence of hidden vectors with the same length. We use INLINEFORM0 and INLINEFORM1 ( INLINEFORM2 and INLINEFORM3 ) to represent the hidden vector sequence given the question INLINEFORM4 and the document INLINEFORM5 in the target (source) domain respectively.The domain discriminator INLINEFORM0 focuses on identifying the domain of the vector sequence is from given INLINEFORM1 or INLINEFORM2 , where the objective is to minimize INLINEFORM3 . DISPLAYFORM0 Given a training example from the target domain ( INLINEFORM0 ), INLINEFORM1 learns to assign a lower score to INLINEFORM2 and INLINEFORM3 in that example, that is, to minimize INLINEFORM4 and INLINEFORM5 . On the other hand, given a training example from the source domain ( INLINEFORM6 ), INLINEFORM7 learns to assign a larger value to INLINEFORM8 and INLINEFORM9 .Furthermore, we update the parameters of the embedding encoders to maximize the domain classification loss INLINEFORM0 , which works adversarially towards the domain discriminator. We thus expect the model to learn features and structures that can generalize across domains when the outputs of INLINEFORM1 are indistinguishable from the outputs of INLINEFORM2 . The loss function for embedding encoder, INLINEFORM3 , is formulated as DISPLAYFORM0 where INLINEFORM0 is a hyperparameter. The two embedding encoders in the QA model are learned to maximize INLINEFORM1 while minimizing the loss for QA, INLINEFORM2 . Because the parameters of other layers in QA model are independent to the loss of the domain discriminator, the loss function of other layers, INLINEFORM3 , is equivalent to INLINEFORM4 , that is, INLINEFORM5 .Although the discriminator is applied to the output of embedding encoder in Figure FIGREF2 , it can be also applied to other layers. Considering that almost all QA model contains such embedding encoders, the proposed approach is expected to generalize to other QA models in addition to QANet.Spoken-SQuAD is chosen as the target domain data for training and testing. Spoken-SQuAD BIBREF5 is an automatically generated corpus in which the document is in spoken form and the question is in text form. The reference transcriptions are from SQuAD BIBREF1 . There are 37,111 and 5,351 question answer pairs in the training and testing sets respectively, and the word error rate (WER) of both sets is around 22.7%.The original SQuAD, Text-SQuAD, is chosen as the source domain data, where only question answering pairs appearing in Spoken-SQuAD are utilized. In our task setting, during training we train the proposed QA model on both Text-SQuAD and Spoken-SQuAD training sets. While in the testing stage, we evaluate the performance on Spoken-SQuAD testing set.We utilize fasttext BIBREF18 to generate the embeddings of all words from both Text-SQuAD and Spoken-SQuAD. We adopt the phoneme sequence embeddings to replace the original character sequence embeddings using the method proposed by Li et al. BIBREF5 . The source domain model and the target domain model share the same set of word embedding matrix to improve the alignment between these two domains.W-GAN is adopted for our domain discriminator BIBREF19 , which stacks 5 residual blocks of 1D convolutional layers with 96 filters and filter size 5 followed by one linear layer to convert each input vector sequence into one scalar value.All models used in the experiments are trained with batch size 20, using adam with learning rate INLINEFORM0 and the early stop strategy. The dimension of the hidden state is set to 96 for all layers, and the number of self-attention heads is set to 2. The setup is slightly different but better than the setting suggested by the original QAnet.First, we highlight the domain mismatch phenomenon in our experiments shown in Table TABREF9 . Row (a) is when QANet is trained on Text-SQuAD, row (b) is when QANet is trained on Spoken-SQuAD, and row (c) is when QANet is trained on Text-SQuAD and then finetuned on Spoken-SQuAD. The columns show the evaluation on the testing sets of Text-SQuAD and Spoken-SQuAD.It is clear that the performance drops a lot when the training and testing data mismatch, indicating that model training on ASR hypotheses can not generalize well on reference transcriptions. The performance gap is nearly 20% F1 score (72% to 55%). The row (c) shows the improved performance when testing on S-SQuAD due to the transfer learning via fine-tuning.To better demonstrate the effectiveness of the proposed model, we compare with baselines and show the results in Table TABREF12 . The baselines are: (a) trained on S-SQuAD, (b) trained on T-SQuAD and then fine-tuned on S-SQuAD, and (c) previous best model trained on S-SQuAD BIBREF5 by using Dr.QA BIBREF20 . We also compare to the approach proposed by Lan et al. BIBREF16 in the row (d). This approach is originally proposed for spoken language understanding, and we adopt the same approach on the setting here. The approach models domain-specific features from the source and target domains separately by two different embedding encoders with a shared embedding encoder for modeling domain-general features. The domain-general parameters are adversarially trained by domain discriminator.Row (e) is the model that the weights of all layers are tied between the source domain and the target domain. Row (f) uses the same architecture as row (e) with an additional domain discriminator applied to the embedding encoder. It can be found that row (f) outperforms row (e), indicating that the proposed domain adversarial learning is helpful. Therefore, our following experiments contain domain adversarial learning. The proposed approach (row (f)) outperforms previous best model (row (c)) by 2% EM score and over 1.5% F1 score. We also show the results of applying the domain discriminator to the top of context query attention layer in row (g), which obtains poor performance. To sum it up, incorporating adversarial learning by applying the domain discriminator on top of the embedding encoder layer is effective.Layer weight tying or untying within the model indicates different levels of symmetric mapping between the source and target domains. Different combinations are investigated and shown in Table TABREF14 . The row (a) in which all layers are tied is the row (e) of Table TABREF12 . The results show that untying context-query attention layer L2 (rows (c, f, g)) or model encoder layer L3 (rows (d, f, h)) lead to degenerated solutions in comparison to row (a) where all layers are tied. Untying both of them simultaneously leads to the worst performance which is even worse than the finetuning (row (g) v.s. (c) from Table TABREF12 ). These results imply that sharing the context-query attention layer and the model encoder layer are important for domain adaptation on SQA. We conjecture that these two layers benefit from training on source domain data where there are no ASR errors, so the QA model learns to conduct attention or further reason well on target domain data with ASR errors.Overall, it is not beneficial to untie any layer, because no information can be shared across different domains. Untying the embedding encoder L1 and the output layer L4 leads to the least degradation in comparison to row (a).In this work, we incorporate a domain discriminator to align the mismatched domains between ASR hypotheses and reference transcriptions. The adversarial learning allows the end-to-end QA model to learn domain-invariant features and improve the robustness to ASR errors. The experiments demonstrate that the proposed model successfully achieves superior performance and outperforms the previous best model by 2% EM score and over 1.5% F1 score.

Q: What evaluation metrics were used?

Q: What was the score of the proposed model?
A: Best results authors obtain is EM 51.10 and F1 63.11
EM Score of 51.10

Q: What was the previous best model?

Q: Which datasets did they use for evaluation?