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SDNet: Contextualized Attention-based Deep Network for Conversational
Question AnsweringJan. 31, 2019
Chenguang ZhuMicrosoft Speech and Dialogue Research Group
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Motivation
• Traditional MRC models target for single-turn QA scenario
• No correlation between different questions/answers
• Humans interact by conversation, with rich context
• Models with conversational AI better match realistic settings, like customer support chatbot, social chatbot
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CoQA Dataset
• From Stanford NLP Lab
• First conversational question and answering datasets
• Context can be carried over, requiring coreference resolution
• Model should comprehend both the passage and questions/answers from previous rounds
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Example
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Our formulation
• Passage (context): • Previous rounds of questions and answers: • Current question:
• We prepend the latest rounds of QAs to the current question:•
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Encoding Layer
• For each word in passage and question• 300-dim GloVe word embedding• 1024-dim BERT contextual embedding
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Using BERT
• We utilize BERT as a contextual embedder, with some adaptation
• BERT tokenizes text into byte pair encoding (BPE)
• It may not align with canonical tokenizers like spaCy
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Using BERT--Alignment
• Tokenize text into word sequence
• Then use BERT to tokenize each word into BPEs:•
• After obtaining contextual embedding for each BPE, we average the BERT embeddings for all the BPEs corresponding to a word
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Using BERT—Linear combination
• BERT has many layers and the paper recommends using only the last layer
• We linearly combine all layers of output
• This can greatly improve the results
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Using BERT—Linear combination
Linear combination Averaged BPE embedding for a word
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Using BERT– Lock internal weights
• Locking weights of BERT (no backpropagation)
• In our case, finetuning does not work well
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Representation
• Each word in passage is represented by
• Each word in question is represented by
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Integration Layer
• Word-level Inter-Attention• Attend from question to passage, i.e. :
• Passage words have several more features• POS, NER embedding• 3-dim exact matching vector• A normalized term frequency
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RNN
• Passage (context) and question words both go through K layers of Bi-LSTMs
• Question words go through one more RNN layer
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Question self-attention
• Question contains conversation history• We need to establish relationship between history and current question• Self-attention:
, , ,
• is the final representation of question words
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Multi-level inter-attention
• Adapted from FusionNet (Huang et al. 2018)
• After multi-level inter-attention, we conduct RNN, self-attention and another RNN to obtain the final representation of context:
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Generate answer – positional probability
• Firstly, we condense the question into one vector
• Then we generate the probability that the answer starts and ends at each position:
Start probability:
GRU: End probability:
• We generate the probability of “yes”, “no”, “no answer” similarly
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Overall structure -- SDNet
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Training and prediction
• Loss is cross-entropy• Batch size: 1 dialogue• 30 epochs• ~2 hours per epoch on one Volta-100 GPU
• Prediction of span is done by choosing maximum (start x end) probability, with length <= 15
• Ensemble model consists of 12 best single models with different random seeds
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Experiments
• CoQA dataset• Dev set: 100 passages per in-domain topics• Test set: 100 passages per in-domain and out-of-domain topics
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Leaderboard (As of 12/11/2018)
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Ablation Studies
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Effect of history
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Training
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Summary
• SDNet leverages MRC, BERT and conversational history
• BERT is important, but adaptation is required to use it as a module
• Next step is open-domain conversational QA, with no passage given, and model may also ask questions
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Introduction to SDRG
• Microsoft Speech and Dialogue Research Group
• Conduct research on speech recognition and conversational AI
• We’re from Stanford, Princeton, Cambridge, IBM, …
• We’re now hiring researchers and machine learning engineers
• We have world-class research environment• DGX-1 machine and large clusters with tons of GPUs
• Welcome to learn more!
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Q&A
• Thank you!
• My Email: [email protected]
• More details in our paper:• SDNet: Contextualized Attention-based Deep Network for Conversational
Question Answering – Chenguang Zhu, Michael Zeng, Xuedong Huang• https://arxiv.org/abs/1812.03593
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