Publications

Contextualized Non-local Neural Networks for Sequence Learning
Pengfei Liu
Shuaichen Chang
Xuanjing Huang
Jian Tang
Jackie CK Cheung
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which selfattention, as exemplified by… (voir plus) the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN3), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighbourhood.Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labelling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Generating Character Descriptions for Automatic Summarization of Fiction
Weiwei Zhang
Jackie CK Cheung
J. Oren
Summaries of fictional stories allow readers to quickly decide whether or not a story catches their interest. A major challenge in automatic… (voir plus) summarization of fiction is the lack of standardized evaluation methodology or high-quality datasets for experimentation. In this work, we take a bottomup approach to this problem by assuming that story authors are uniquely qualified to inform such decisions. We collect a dataset of one million fiction stories with accompanying author-written summaries from Wattpad, an online story sharing platform. We identify commonly occurring summary components, of which a description of the main characters is the most frequent, and elicit descriptions of main characters directly from the authors for a sample of the stories. We propose two approaches to generate character descriptions, one based on ranking attributes found in the story text, the other based on classifying into a list of pre-defined attributes. We find that the classification-based approach performs the best in predicting character descriptions.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Learning Multi-Task Communication with Message Passing for Sequence Learning
Pengfei Liu
Jie Fu
Yue Dong
Xipeng Qiu
Jackie CK Cheung
We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different ta… (voir plus)sks to be learned dynamically, rather than using an ad-hoc pre-defined structure as in previous work. We adopt the idea from message-passing graph neural networks, and propose a general graph multi-task learning framework in which different tasks can communicate with each other in an effective and interpretable way. We conduct extensive experiments in text classification and sequence labelling to evaluate our approach on multi-task learning and transfer learning. The empirical results show that our models not only outperform competitive baselines, but also learn interpretable and transferable patterns across tasks.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Learning Options with Interest Functions
Khimya Khetarpal
Doina Precup
Learning temporal abstractions which are partial solutions to a task and could be reused for solving other tasks is an ingredient that can h… (voir plus)elp agents to plan and learn efficiently. In this work, we tackle this problem in the options framework. We aim to autonomously learn options which are specialized in different state space regions by proposing a notion of interest functions, which generalizes initiation sets from the options framework for function approximation. We build on the option-critic framework to derive policy gradient theorems for interest functions, leading to a new interest-option-critic architecture.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Leveraging Observations in Bandits: Between Risks and Benefits.
Andrei Lupu
Audrey Durand
Doina Precup
Imitation learning has been widely used to speed up learning in novice agents, by allowing them to leverage existing data from experts. Allo… (voir plus)wing an agent to be influenced by external observations can benefit to the learning process, but it also puts the agent at risk of following sub-optimal behaviours. In this paper, we study this problem in the context of bandits. More specifically, we consider that an agent (learner) is interacting with a bandit-style decision task, but can also observe a target policy interacting with the same environment. The learner observes only the target’s actions, not the rewards obtained. We introduce a new bandit optimism modifier that uses conditional optimism contingent on the actions of the target in order to guide the agent’s exploration. We analyze the effect of this modification on the well-known Upper Confidence Bound algorithm by proving that it preserves a regret upper-bound of order O(lnT), even in the presence of a very poor target, and we derive the dependency of the expected regret on the general target policy. We provide empirical results showing both great benefits as well as certain limitations inherent to observational learning in the multi-armed bandit setting. Experiments are conducted using targets satisfying theoretical assumptions with high probability, thus narrowing the gap between theory and application.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
On-line Adaptative Curriculum Learning for GANs
Thang Doan
Joao Monteiro
Isabela Albuquerque
Bogdan Mazoure
Audrey Durand
Joelle Pineau
R Devon Hjelm
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples. However, open … (voir plus)questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning. To this end, we propose a reward function which reflects the progress made by the generator and dynamically update the mixture weights allocated to each discriminator. We also draw connections between our algorithm and stochastic optimization methods and then show that existing approaches using multiple discriminators in literature can be recovered from our framework. We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support. On the other hand, highly expressive discriminators ensure samples quality. Finally, experimental results show that our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum. These results also support the claim that weaker discriminators have higher entropy improving modes coverage. Keywords: multiple discriminators, curriculum learning, multiple resolutions discriminators, multi-armed bandits, generative adversarial networks, smooth discriminators, multi-discriminator gan training, multiple experts.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies
Sarath Chandar
Chinnadhurai Sankar
Eugene Vorontsov
Samira Ebrahimi Kahou
Yoshua Bengio
Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish durin… (voir plus)g training, as the sequence length increases. Gradients can be attenuated by transition operators and are attenuated or dropped by activation functions. Canonical architectures like LSTM alleviate this issue by skipping information through a memory mechanism. We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients. In a series of synthetic and real world tasks, we demonstrate that the proposed model is the only model that performs among the top 2 models across all tasks with and without long-term dependencies, when compared against a range of other architectures.
2019-07-16
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Towards Understanding Generalization in Gradient-Based Meta-Learning
Simon Guiroy
Vikas Verma
Christopher Pal
In this work we study generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective lan… (voir plus)dscapes. We experimentally demonstrate that as meta-training progresses, the meta-test solutions, obtained after adapting the meta-train solution of the model, to new tasks via few steps of gradient-based fine-tuning, become flatter, lower in loss, and further away from the meta-train solution. We also show that those meta-test solutions become flatter even as generalization starts to degrade, thus providing an experimental evidence against the correlation between generalization and flat minima in the paradigm of gradient-based meta-leaning. Furthermore, we provide empirical evidence that generalization to new tasks is correlated with the coherence between their adaptation trajectories in parameter space, measured by the average cosine similarity between task-specific trajectory directions, starting from a same meta-train solution. We also show that coherence of meta-test gradients, measured by the average inner product between the task-specific gradient vectors evaluated at meta-train solution, is also correlated with generalization. Based on these observations, we propose a novel regularizer for MAML and provide experimental evidence for its effectiveness.
2019-07-15
ArXiv (prépublication)
openreview.net
openreview.net
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
Meng Qu
Jian Tang
Yoshua Bengio
This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the ali… (voir plus)gnment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obtain in many domains. Therefore, in this paper we propose to study aligning knowledge graphs in fully-unsupervised or weakly-supervised fashion, i.e., without or with only a few aligned triplets. We propose an unsupervised framework to align the entity and relation embddings of different knowledge graphs with an adversarial learning framework. Moreover, a regularization term which maximizes the mutual information between the embeddings of different knowledge graphs is used to mitigate the problem of mode collapse when learning the alignment functions. Such a framework can be further seamlessly integrated with existing supervised methods by utilizing a limited number of aligned triples as guidance. Experimental results on multiple datasets prove the effectiveness of our proposed approach in both the unsupervised and the weakly-supervised settings.
2019-07-05
ArXiv (prépublication)
openreview.net
openreview.net
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning
Srinivas Venkattaramanujam
Eric Crawford
Thang Doan
Doina Precup
Goal-conditioned policies are used in order to break down complex reinforcement learning (RL) problems by using subgoals, which can be defin… (voir plus)ed either in state space or in a latent feature space. This can increase the efficiency of learning by using a curriculum, and also enables simultaneous learning and generalization across goals. A crucial requirement of goal-conditioned policies is to be able to determine whether the goal has been achieved. Having a notion of distance to a goal is thus a crucial component of this approach. However, it is not straightforward to come up with an appropriate distance, and in some tasks, the goal space may not even be known a priori. In this work we learn a distance-to-goal estimate which is computed in terms of the number of actions that would need to be carried out in a self-supervised approach. Our method solves complex tasks without prior domain knowledge in the online setting in three different scenarios in the context of goal-conditioned policies a) the goal space is the same as the state space b) the goal space is given but an appropriate distance is unknown and c) the state space is accessible, but only a subset of the state space represents desired goals, and this subset is known a priori. We also propose a goal-generation mechanism as a secondary contribution.
2019-07-04
ArXiv (prépublication)
arxiv.org
arxiv.org
A Cross-Domain Transferable Neural Coherence Model
Peng Xu
Hamidreza Saghir
Jin Sung Kang
Teng Long
Avishek Joey Bose
Yanshuai Cao
Jackie Chi Kit Cheung
Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence… (voir plus) models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.
2019-06-30
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
Yue Dong
Zichao Li
Mehdi Rezagholizadeh
Jackie CK Cheung
We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-inte… (voir plus)rpreter approach. Most current neural sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. These methods learn to simplify sentences as a byproduct of the fact that they are trained on complex-simple sentence pairs. By contrast, our neural programmer-interpreter is directly trained to predict explicit edit operations on targeted parts of the input sentence, resembling the way that humans perform simplification and revision. Our model outperforms previous state-of-the-art neural sentence simplification models (without external knowledge) by large margins on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, +1.41 Newsela), and is judged by humans to produce overall better and simpler output sentences.
2019-06-30
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org