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Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
TLDR
The proposed sequential latent variable model can keep track of the prior and posterior distribution over knowledge and can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge.
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AudioCaps: Generating Captions for Audios in The Wild
TLDR
A large-scale dataset of 46K audio clips with human-written text pairs collected via crowdsourcing on the AudioSet dataset is contributed and two novel components that help improve audio captioning performance are proposed: the top-down multi-scale encoder and aligned semantic attention.
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Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
TLDR
This work collects Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit, and proposes a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi- level memory to store the information of text from different levels of abstraction.
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Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
TLDR
This work proposes a novel captioning model named Context Sequence Memory Network (CSMN), and shows the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.
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Towards Personalized Image Captioning via Multimodal Memory Networks
TLDR
Qualitative evaluation and user studies via Amazon Mechanical Turk show that the three novel features of the CSMN help enhance the performance of personalized image captioning over state-of-the-art captioning models.
View on IEEE
How Robust are Fact Checking Systems on Colloquial Claims?
TLDR
It is found that existing fact checking systems that perform well on claims in formal style significantly degenerate on colloquial claims with the same semantics, and it is shown that document retrieval is the weakest spot in the system even vulnerable to filler words, such as “yeah” and “you know”.
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Will I Sound like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
TLDR
Inspired by social cognition and pragmatics, existing dialogue agents are endow with public self-consciousness on the fly through an imaginary listener to enforce dialogue agents to refrain from uttering contradiction and improve consistency of existing dialogue models.
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Public Self-consciousness for Endowing Dialogue Agents with Consistent Persona
TLDR
This approach, based on the Rational Speech Acts framework, attempts to maintain consistency in an unsupervised manner requiring neither additional annotations nor pretrained external models to improve consistency in dialogue agents.
View PDF on arXiv
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