Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions.Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently.In this paper, we introduce a new follow-up question identification task.We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question.It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question.Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.

Ensuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system.Most prior work on dialogue research does not focus on preventing dialogue breakdown.One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality.Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation.In this paper, we propose a novel dialogue breakdown detection model that jointly incorporates a pretrained cross-lingual language model and a co-attention network.Our proposed model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations.Co-attention aims to capture the interaction between the latest utterance and the conversation history, and thereby determines whether the latest utterance causes a dialogue breakdown.Experimental results show that our proposed model outperforms all previous approaches on all evaluation metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 (DBDC4 at IWSDS2019).

We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question.Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text.It generates potential paths through passages and scores them without any direct path supervision.The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path.To capture additional context, Path-Net also composes the passage representations along each path to compute a passage-based representation.Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages.We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching stateof-the-art performance.

Neural network models recently proposed for question answering (QA) primarily focus on capturing the passage-question relation. However, they have minimal capability to link relevant facts distributed across multiple sentences which is crucial in achieving deeper understanding, such as performing multi-sentence reasoning, co-reference resolution, etc. They also do not explicitly focus on the question and answer type which often plays a critical role in QA. In this paper, we propose a novel end-to-end question-focused multi-factor attention network for answer extraction. Multi-factor attentive encoding using tensor-based transformation aggregates meaningful facts even when they are located in multiple sentences. To implicitly infer the answer type, we also propose a max-attentional question aggregation mechanism to encode a question vector based on the important words in a question. During prediction, we incorporate sequence-level encoding of the first wh-word and its immediately following word as an additional source of question type information. Our proposed model achieves significant improvements over the best prior state-of-the-art results on three large-scale challenging QA datasets, namely NewsQA, TriviaQA, and SearchQA.

Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA).However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage.A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage.In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer.If the associated passage does not contain any valid answer, the QA system will correctly return Nil.We propose a nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step.We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an endto-end fashion.Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer.Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.

Deep neural networks (DNNs) for acoustic modeling have been shown to provide impressive results on many state-of-the-art automatic speech recognition (ASR) applications. However, DNN performance degrades due to mismatches in training and testing conditions and thus adaptation is necessary. In this paper, we explore the use of discriminative auxiliary input features obtained using joint acoustic factor learning for DNN adaptation. These features are derived from a bottleneck (BN) layer of a DNN and are referred to as BN vectors. To derive these BN vectors, we explore the use of two types of joint acoustic factor learning which capture speaker and auxiliary information such as noise, phone and articulatory information of speech. In this paper, we show that these BN vectors can be used for adaptation and thereby improve the performance of an ASR system. We also show that the performance can be further improved on augmenting these BN vectors to conventional i-vectors. In this paper, experiments are performed on Aurora-4, REVERB challenge and AMI databases.

Long Short-Term Memory (LSTM) is a particular type of recurrent neural network (RNN) that can model long term temporal dynamics. Recently it has been shown that LSTM-RNNs can achieve higher recognition accuracy than deep feed-forword neural networks (DNNs) in acoustic modelling. However, speaker adaption for LSTM-RNN based acoustic models has not been well investigated. In this paper, we study the LSTM-RNN speaker-aware training that incorporates the speaker information during model training to normalise the speaker variability. We first present several speaker-aware training architectures, and then empirically evaluate three types of speaker representation: I-vectors, bottleneck speaker vectors and speaking rate. Furthermore, to factorize the variability in the acoustic signals caused by speakers and phonemes respectively, we investigate the speaker-aware and phone-aware joint training under the framework of multi-task learning. In AMI meeting speech transcription task, speaker-aware training of LSTM-RNNs reduces word error rates by 6.5% relative to a very strong LSTM-RNN baseline, which uses FMLLR features.