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Publications
SPE: Symmetrical Prompt Enhancement for Factual Knowledge Retrieval
Pretrained language models (PLMs) have 001 been shown to accumulate factual knowledge 002 from their unsupervised pretraining proce-003 dure… (voir plus)s (Petroni et al., 2019). Prompting is an 004 effective way to query such knowledge from 005 PLMs. Recently, continuous prompt methods 006 have been shown to have a larger potential 007 than discrete prompt methods in generating ef-008 fective queries (Liu et al., 2021a). However, 009 these methods do not consider symmetry of 010 the task. In this work, we propose Symmet-011 rical Prompt Enhancement (SPE), a continu-012 ous prompt-based method for fact retrieval that 013 leverages the symmetry of the task. Our results 014 on LAMA, a popular fact retrieval dataset, 015 show significant improvement of SPE over pre-016 vious prompt methods
Systematic generalisation with group invariant predictions
We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-train… (voir plus)ed neural network to be less reliant on more persistently correlating complex features. When the non-persistent, simpler correlations correspond to non-semantic background factors, a neural network trained on this data can exhibit dramatic failure upon encountering systematic distributional shift, where the correlating background features are recombined with different objects. We perform an empirical study on three synthetic datasets, showing that group invariance methods across inferred partitionings of the training set can lead to significant improvements at such test-time situations. We also suggest a simple invariance penalty, showing with experiments on our setups that it can perform better than alternatives. We find that even without assuming access to any systematically shifted validation sets, one can still find improvements over an ERM-trained reference model.
The Situated Interactive Multi-Modal Conver-001 sations (SIMMC) 2.0 aims to create virtual 002 shopping assistants that can accept complex 0… (voir plus)03 multi-modal inputs, i.e. visual appearances of 004 objects and user utterances. It consists of four 005 subtasks, multi-modal disambiguation (MM-006 Disamb), multi-modal coreference resolution 007 (MM-Coref), multi-modal dialog state tracking 008 (MM-DST), and response retrieval and genera-009 tion. While many task-oriented dialog systems 010 usually tackle each subtask separately, we pro-011 pose a jointly learned encoder-decoder that per-012 forms all four subtasks at once for efficiency. 013 Moreover, we handle the multi-modality of the 014 challenge by representing visual objects as spe-015 cial tokens whose joint embedding is learned 016 via auxiliary tasks. This approach won the MM-017 Coref and response retrieval subtasks and nom-018 inated runner-up for the remaining subtasks 019 using a single unified model. In particular, 020 our model achieved 81.5% MRR, 71.2% R@1, 021 95.0% R@5, 98.2% R@10, and 1.9 mean rank 022 in response retrieval task, setting a high bar for 023 the state-of-the-art result in the SIMMC 2.0 024 track of the Dialog Systems Technology Chal-025 lenge 10 (DSTC10). 026
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement l… (voir plus)earning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a notion of affordances for options, and develop temporally abstract partial option models, that take into account the fact that an option might be affordable only in certain situations. We analyze the trade-offs between estimation and approximation error in planning and learning when using such models, and identify some interesting special cases. Additionally, we empirically demonstrate the ability to learn both affordances and partial option models online resulting in improved sample efficiency and planning time in the Taxi domain.
Natural language processing systems such as dialogue agents should be able to reason about other people’s beliefs, intentions and desires.… (voir plus) This capability, called theory of mind (ToM), is crucial, as it allows a model to predict and interpret the needs of users based on their mental states. A recent line of research evaluates the ToM capability of existing memoryaugmented neural models through questionanswering. These models perform poorly on false belief tasks where beliefs differ from reality, especially when the dataset contains distracting sentences. In this paper, we propose a new temporally informed approach for improving the ToM capability of memory-augmented neural models. Our model incorporates priors about the entities’ minds and tracks their mental states as they evolve over time through an extended passage. It then responds to queries through textual time travel—i.e., by accessing the stored memory of an earlier time step. We evaluate our model on ToM datasets and find that this approach improves performance, particularly by correcting the predicted mental states to match the false belief.
2021-01-01
Conference on Empirical Methods in Natural Language Processing (publié)
Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way … (voir plus)to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of"task"that might be desirable: (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists. We conclude with an empirical study that corroborates and illustrates our theoretical findings.
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. Researc… (voir plus)hers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether unseen topics are used in the testing phase. However, neither scenario allows us to explain whether errors are caused by failure to capture authorship style, by the topic shift or by other factors. Motivated by this, we propose the topic confusion task, where we switch the author-topic config-uration between training and testing set. This setup allows us to probe errors in the attribution process. We investigate the accuracy and two error measures: one caused by the models’ confusion by the switch because the features capture the topics, and one caused by the features’ inability to capture the writing styles, leading to weaker models. By evaluating different features, we show that stylometric features with part-of-speech tags are less susceptible to topic variations and can increase the accuracy of the attribution process. We further show that combining them with word-level n - grams can outperform the state-of-the-art technique in the cross-topic scenario. Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task, and are outperformed by simple n -gram features.
The problem of characterizing quantum channels arises in a number of contexts such as quantum process tomography and quantum error correctio… (voir plus)n. However, direct approaches to parameterizing and optimizing the Choi matrix representation of quantum channels face a curse of dimensionality: the number of parameters scales exponentially in the number of qubits. Recently, Torlai et al. [2020] proposed using locally purified density operators (LPDOs), a tensor network representation of Choi matrices, to overcome the unfavourable scaling in parameters. While the LPDO structure allows it to satisfy a ‘complete positivity’ (CP) constraint required of physically valid quantum channels, it makes no guarantees about a similarly required ‘trace preservation’ (TP) constraint. In practice, the TP constraint is violated, and the learned quantum channel may even be trace-increasing, which is non-physical. In this work, we present the problem of optimizing over TP LPDOs, discuss two approaches to characterizing the TP constraints on LPDOs, and outline the next steps for developing an optimization scheme.
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Meta and transfer learning are two successful families of approaches to few-shot 1 learning. Despite highly related goals, state-of-the-art … (voir plus)advances in each family are 2 measured largely in isolation of each other. As a result of diverging evaluation 3 norms, a direct or thorough comparison of different approaches is challenging. 4 To bridge this gap, we introduce a few-shot classification evaluation protocol 5 named VTAB+MD with the explicit goal of facilitating sharing of insights from 6 each community. We demonstrate its accessibility in practice by performing a 7 cross-family study of the best transfer and meta learners which report on both a 8 large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning 9 benchmark (Visual Task Adaptation Benchmark, VTAB). We find that, on average, 10 large-scale transfer methods (Big Transfer, BiT) outperform competing approaches 11 on MD, even when trained only on ImageNet. In contrast, meta-learning approaches 12 struggle to compete on VTAB when trained and validated on MD. However, BiT 13 is not without limitations, and pushing for scale does not improve performance 14 on highly out-of-distribution MD tasks. We hope that this work contributes to 15 accelerating progress on few-shot learning research. 16
