7 research outputs found
Personalizing Dialogue Agents via Meta-Learning
Existing personalized dialogue models use human designed persona descriptions
to improve dialogue consistency. Collecting such descriptions from existing
dialogues is expensive and requires hand-crafted feature designs. In this
paper, we propose to extend Model-Agnostic Meta-Learning (MAML)(Finn et al.,
2017) to personalized dialogue learning without using any persona descriptions.
Our model learns to quickly adapt to new personas by leveraging only a few
dialogue samples collected from the same user, which is fundamentally different
from conditioning the response on the persona descriptions. Empirical results
on Persona-chat dataset (Zhang et al., 2018) indicate that our solution
outperforms non-meta-learning baselines using automatic evaluation metrics, and
in terms of human-evaluated fluency and consistency.Comment: Accepted in ACL 2019. Zhaojiang Lin* and Andrea Madotto* contributed
equally to this wor
DCA: Diversified Co-Attention towards Informative Live Video Commenting
We focus on the task of Automatic Live Video Commenting (ALVC), which aims to
generate real-time video comments with both video frames and other viewers'
comments as inputs. A major challenge in this task is how to properly leverage
the rich and diverse information carried by video and text. In this paper, we
aim to collect diversified information from video and text for informative
comment generation. To achieve this, we propose a Diversified Co-Attention
(DCA) model for this task. Our model builds bidirectional interactions between
video frames and surrounding comments from multiple perspectives via metric
learning, to collect a diversified and informative context for comment
generation. We also propose an effective parameter orthogonalization technique
to avoid excessive overlap of information learned from different perspectives.
Results show that our approach outperforms existing methods in the ALVC task,
achieving new state-of-the-art results
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Sensational headlines are headlines that capture people's attention and
generate reader interest. Conventional abstractive headline generation methods,
unlike human writers, do not optimize for maximal reader attention. In this
paper, we propose a model that generates sensational headlines without labeled
data. We first train a sensationalism scorer by classifying online headlines
with many comments ("clickbait") against a baseline of headlines generated from
a summarization model. The score from the sensationalism scorer is used as the
reward for a reinforcement learner. However, maximizing the noisy
sensationalism reward will generate unnatural phrases instead of sensational
headlines. To effectively leverage this noisy reward, we propose a novel loss
function, Auto-tuned Reinforcement Learning (ARL), to dynamically balance
reinforcement learning (RL) with maximum likelihood estimation (MLE). Human
evaluation shows that 60.8% of samples generated by our model are sensational,
which is significantly better than the Pointer-Gen baseline and other RL
models.Comment: Accepted by EMNLP201