11 research outputs found
Learning by Asking Questions
We introduce an interactive learning framework for the development and
testing of intelligent visual systems, called learning-by-asking (LBA). We
explore LBA in context of the Visual Question Answering (VQA) task. LBA differs
from standard VQA training in that most questions are not observed during
training time, and the learner must ask questions it wants answers to. Thus,
LBA more closely mimics natural learning and has the potential to be more
data-efficient than the traditional VQA setting. We present a model that
performs LBA on the CLEVR dataset, and show that it automatically discovers an
easy-to-hard curriculum when learning interactively from an oracle. Our LBA
generated data consistently matches or outperforms the CLEVR train data and is
more sample efficient. We also show that our model asks questions that
generalize to state-of-the-art VQA models and to novel test time distributions
Training Curricula for Open Domain Answer Re-Ranking
In precision-oriented tasks like answer ranking, it is more important to rank
many relevant answers highly than to retrieve all relevant answers. It follows
that a good ranking strategy would be to learn how to identify the easiest
correct answers first (i.e., assign a high ranking score to answers that have
characteristics that usually indicate relevance, and a low ranking score to
those with characteristics that do not), before incorporating more complex
logic to handle difficult cases (e.g., semantic matching or reasoning). In this
work, we apply this idea to the training of neural answer rankers using
curriculum learning. We propose several heuristics to estimate the difficulty
of a given training sample. We show that the proposed heuristics can be used to
build a training curriculum that down-weights difficult samples early in the
training process. As the training process progresses, our approach gradually
shifts to weighting all samples equally, regardless of difficulty. We present a
comprehensive evaluation of our proposed idea on three answer ranking datasets.
Results show that our approach leads to superior performance of two leading
neural ranking architectures, namely BERT and ConvKNRM, using both pointwise
and pairwise losses. When applied to a BERT-based ranker, our method yields up
to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model
trained without a curriculum). This results in models that can achieve
comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long
Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation
Current state-of-the-art neural dialogue systems are mainly data-driven and
are trained on human-generated responses. However, due to the subjectivity and
open-ended nature of human conversations, the complexity of training dialogues
varies greatly. The noise and uneven complexity of query-response pairs impede
the learning efficiency and effects of the neural dialogue generation models.
What is more, so far, there are no unified dialogue complexity measurements,
and the dialogue complexity embodies multiple aspects of
attributes---specificity, repetitiveness, relevance, etc. Inspired by human
behaviors of learning to converse, where children learn from easy dialogues to
complex ones and dynamically adjust their learning progress, in this paper, we
first analyze five dialogue attributes to measure the dialogue complexity in
multiple perspectives on three publicly available corpora. Then, we propose an
adaptive multi-curricula learning framework to schedule a committee of the
organized curricula. The framework is established upon the reinforcement
learning paradigm, which automatically chooses different curricula at the
evolving learning process according to the learning status of the neural
dialogue generation model. Extensive experiments conducted on five
state-of-the-art models demonstrate its learning efficiency and effectiveness
with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation
Non-autoregressive translation (NAT) models remove the dependence on previous
target tokens and generate all target tokens in parallel, resulting in
significant inference speedup but at the cost of inferior translation accuracy
compared to autoregressive translation (AT) models. Considering that AT models
have higher accuracy and are easier to train than NAT models, and both of them
share the same model configurations, a natural idea to improve the accuracy of
NAT models is to transfer a well-trained AT model to an NAT model through
fine-tuning. However, since AT and NAT models differ greatly in training
strategy, straightforward fine-tuning does not work well. In this work, we
introduce curriculum learning into fine-tuning for NAT. Specifically, we design
a curriculum in the fine-tuning process to progressively switch the training
from autoregressive generation to non-autoregressive generation. Experiments on
four benchmark translation datasets show that the proposed method achieves good
improvement (more than BLEU score) over previous NAT baselines in terms of
translation accuracy, and greatly speed up (more than times) the inference
process over AT baselines.Comment: AAAI 202
An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications
Both humans and machines learn the meaning of unknown words through
contextual information in a sentence, but not all contexts are equally helpful
for learning. We introduce an effective method for capturing the level of
contextual informativeness with respect to a given target word. Our study makes
three main contributions. First, we develop models for estimating contextual
informativeness, focusing on the instructional aspect of sentences. Our
attention-based approach using pre-trained embeddings demonstrates
state-of-the-art performance on our single-context dataset and an existing
multi-sentence context dataset. Second, we show how our model identifies key
contextual elements in a sentence that are likely to contribute most to a
reader's understanding of the target word. Third, we examine how our contextual
informativeness model, originally developed for vocabulary learning
applications for students, can be used for developing better training curricula
for word embedding models in batch learning and few-shot machine learning
settings. We believe our results open new possibilities for applications that
support language learning for both human and machine learner