pwc-archive/papers-with-abstracts huggingface dataset:
Importance-related Fillers Improve the Classification Accuracy of the Response Time Concealed Information Test in a Crime Scenario
Authors: Jerzy Wojciechowski, Gáspár Lukács
Date: 2021-05-01 00:00:00
Tasks: Diagnostic, Open-Ended Question Answering

Purpose. The Response Time Concealed Information Test (RT-CIT) can reveal when a person recognizes a relevant item among other irrelevant items, based on comparatively slower responding. Therefore, if a person is concealing knowledge about the relevance of this item (e.g., recognizing it as a murder weapon), this deception can be revealed. A recent study introduced additional “familiarity-related fillers”, and these items substantially enhanced diagnostic efficiency in detecting autobiographical data. However, the generalizability of the efficiency of fillers to other scenarios remains an open question. We empirically investigated whether new importance-related fillers enhanced diagnostic efficiency in an imaginary crime scenario. 
Methods. Two hundred and thirty-nine volunteers participated in an independent samples experiment. Participants were asked to imagine either committing a crime (“guilty” group) or to imagine visiting a museum (“innocent” group). Then, all participants underwent RT-CIT testing using either a standard single probe or an enhanced single probe (with importance-related fillers) protocol.
Results. The enhanced RT-CIT (with importance-related fillers) showed high diagnostic efficiency (AUC = .810), and significantly outperformed the standard version (AUC = .562). Neither dropout rates nor exclusion criteria influenced this enhancement.
Conclusions. Importance-related fillers improve diagnostic efficiency when detecting episodic information using the RT-CIT, and seem to be useful in detecting knowledge in a wide range of scenarios.
HARM: Hardware-assisted Continuous Re-randomization for Microcontrollers
Authors: Anonymous
Date: 2021-07-24 00:00:00

Microcontroller-based embedded systems (MCUs) have become ubiquitous with the emergence of IoT technology. Given its critical roles in many applications, its security is becoming increasingly important. Unfortunately, MCU devices are especially vulnerable. Code reuse attacks are particularly noteworthy since the memory address of firmware code is static. This work seeks to combat code reuse attacks, including ROP and more advanced JIT-ROP via continuous randomization. Previous proposals are geared towards full-fledged OSs with rich runtime environments, and therefore cannot be applied to MCUs. We propose the first solution for ARM-based MCUs leveraging the ARM TrustZone extension. Our system, named HARM, comprises a secure runtime and a binary analysis tool with rewriting module. The secure runtime, protected inside the secure world, proactively triggers and performs non-bypassable randomization to the firmware running in a sandbox in the normal world. Our system does not rely on any firmware feature, and therefore is generally applicable to both bare-metal and RTOS-powered firmware. We have implemented a HARM prototype on a real development board. Our evaluation results indicate that the proposed system can effectively thaw code reuse attacks while keeping the performance and energy overhead low.
Semantic Evaluation for Text-to-SQL with Distilled Test Suite
Authors: Ruiqi Zhong, Tao Yu, Dan Klein
Date: 2020-07-02 00:00:00
Tasks: Semantic Parsing, Text to SQL, Text-To-SQL

We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models, where a predicted query is semantically correct if its denotation is the same as the gold for every possible database. Our method distills a small test suite of databases that achieves high code coverage for the gold query from a large number of randomly generated databases. At evaluation time, it computes the denotation accuracy of the predicted queries on the distilled test suite, hence calculating a tight upper-bound for semantic accuracy efficiently. We generate a distilled test suite for SPIDER, COSQL, and SPARC, and evaluate 21 models submitted to the SPIDER leaderboard. We manually examine 100 predictions where our approach disagrees with the current metric, and verify that our method is always correct. The current metric of SPIDER leads to a 2.5% false negative rate on average and 8.1% in the worst case, indicating that test suite accuracy is needed to reflect progress in semantic parsing better.
Recursive Tree Attention: Improving Semantic Representations with Syntactic Tree Structured Attention Mechanism
Authors: Anonymous
Date: 2021-06-04 00:00:00
Tasks: Constituency Parsing, Representation Learning, Sentence, text-classification, Text Classification

Attention mechanism has shown its effectiveness in state-of-the-art methods on various tasks in natural language processing (NLP). However, these methods are still using attention mechanism in plain, linear topological structures while the syntactic structures of natural languages are known to be hierarchical. Modeling in such syntactic structures like syntactic trees is proved to be superior in semantic representation learning. In this paper, to improve semantic representation learning by modelling attention mechanism in syntactic structures, we propose recursive tree attention, a syntactic tree structured attention mechanism which recursively calculate attention weights and  summarize information through constituency parsing trees. The information of the children is cursively summarized to their parent through the constituency parsing tree. Then the summarized information from the root node is used as the semantic representations of the current sentence. Experimental results on the text classification tasks demonstrate the effectiveness of our proposed attention mechanism. The approaches with recursive tree attention outperform conventional attention mechanism and other syntactic tree based approaches.
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation
Authors: Anonymous
Date: 2021-06-20 00:00:00
Tasks: Style Transfer, Text Generation, Text Summarization

Natural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text. The complexity makes automatic evaluation of NLG particularly challenging. Previous work has typically focused on a single task and developed individual evaluation metrics based on specific intuitions. In this paper, we propose a unifying perspective based on the nature of information change in NLG tasks, including compression (e.g., summarization), transduction (e.g., text rewriting), and creation (e.g., dialog). Information alignment between input, context and output text plays a common central role in characterizing the generation. With automatic alignment prediction models, we develop a family of interpretable metrics that are suitable for evaluating key aspects of different NLG tasks. Experiments show the uniformly designed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in each of diverse tasks, including text summarization, style transfer, and knowledge-grounded dialog.
Modeling Worlds in Text
Authors: Anonymous
Date: 2021-05-21 00:00:00
Tasks: Action Parsing, Knowledge Graphs, Question Answering

We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives---or text-adventure games---are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects---each with their own unique descriptions---providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds. Our dataset provides 24198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7836 heldout instances over 9 additional games in the test set. We further provide baseline models using rules-based, question-answering, and sequence learning approaches in addition to an analysis of the data and corresponding learning tasks.
MixQA: Embedding and Answer Mixing for Question Answering
Authors: Anonymous
Date: 2021-05-27 00:00:00
Tasks: Question Answering

When we attempt to search for answers to our questions in search engines, we often attempt to rephrase our questions if the answers are not what we expect. Therefore, we attempt to replicate this thought process with our method of mixing embeddings of questions and answer ensembling, which we call MixQA. We experiment on the Squad1.1 and Squad2.0 datasets using BERT. We investigate a variety of different questions to mix with the given test-time question, including similar training-time questions, randomly chosen training-time questions, and paraphrases of the given question. We find that all our methods increase performance over the baselines with no mixing, and do indeed take advantage of the information from the mixed questions. Our best performing method on both datasets involves using ROUGE-1 retrieved similar training-time questions for mixing, improving performance on Squad1.1 and Squad2.0.
AGREEMENT AMONG HUMAN AND AUTOMATED TRANSCRIPTIONS OF GLOBAL SONGS
Authors: Anonymous
Date: 2021-05-24 00:00:00
Tasks: Music Transcription

Cross-cultural musical analysis requires standardized symbolic representation of sounds such as score notation. However, transcription into notation is usually conducted manually by ear, which is time-consuming and subjective. Our aim is to evaluate the reliability of existing methods for transcribing songs from diverse societies. We had 3 experts independently transcribe a sample of 32 excerpts of traditional monophonic songs from around the world (half a cappella, half with instrumental accompaniment). 16 songs also had pre-existing transcriptions created by 3 different experts. We compared these human transcriptions against one another and against 10 automatic music transcription algorithms. We found that human transcriptions can be sufficiently reliable (~90% agreement, κ ~.7), but current automated methods are not (<60% agreement, κ <.4). No automated method clearly outperformed others, in contrast to our predictions. These results suggest that improving automated methods for cross-cultural music transcription is critical for diversifying MIR.
GradMask: Effective Fine-tuning on Large-scale Pretrained Language Models via Gradient Masking
Authors: Anonymous
Date: 2021-05-24 00:00:00

Pretrained language models have dominated a variety of NLP tasks.
However, fine-tuning large pretrained models on downstream tasks tend to achieve degenerated and unstable results,
especially when there are only a limited number of training examples available. 
Common ways to solve the problem are via regularization like Dropout or other methods designed specially for fine-tuning, which are mainly applied during forward propagation. 
Orthogonal to them, we propose GradMask, a simple regularization during backward propagation.
GradMask masks part of gradients of model parameters to zero after gradients computation, which prevents the overfitting on downstream tasks, and enables the model to explore potentially better solutions.
We also propose a layer-adaptive masking strategy to fully transfer knowledge from pretrained models.
Experiments demonstrate that fine-tuning BERT-LARGE with GradMask improves 1.26 mean score in 4 smaller datasets in GLUE, and consistent improvements are also gained with other pretrained models.
Besides, We empirically find fine-tuning with GradMask enhances learning rate robustness, i.e., achieves promising results at a wide range of learning rate from 10^{-5} to 10^{-4}.
ReasonBert: Pre-trained to Reason with Distant Supervision
Authors: Anonymous
Date: 2021-05-23 00:00:00
Tasks: Extractive Question-Answering, Question Answering

We present ReasonBERT, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid contexts. Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBERT achieves remarkable improvement over an array of strong baselines. Few-shot experiments further demonstrate that our pre-training method substantially improves sample efficiency. The pre-trained model is available in Huggingface https://huggingface.co/Anonymous
LMSOC: An approach for socially sensitive pretraining
Authors: Anonymous
Date: 2021-05-22 00:00:00
Tasks: Cloze Test, Graph Representation Learning, Language Modeling, Language Modelling, Representation Learning

While large-scale pretrained language models have been shown to learn effective linguistic representations for many NLP tasks, there remain many real-world contextual aspects of language that current approaches do not capture. For instance, consider a  cloze-test ``I enjoyed the _________ game this weekend'': the correct answer depends heavily on where the speaker is from, when the utterance occurred, and the speaker's broader social milieu and preferences.
Although language depends heavily on the geographical, temporal, and other social contexts of the speaker, these elements have not been incorporated into modern transformer-based language models. We propose a simple but effective approach to incorporate speaker social context into the learned representations of large-scale language models. Our method first learns dense representations of social contexts using graph representation learning algorithms and then primes language model pretraining with these social context representations. We evaluate our approach on geographically-sensitive language-modeling tasks and show a substantial improvement (more than 100% relative lift on MRR) compared to baselines.
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
Authors: Anonymous
Date: 2021-05-18 00:00:00
Tasks: Abstract Meaning Representation, AMR Parsing, Decoder, Sentence

Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing benchmarks. These parsers are simple and avoid explicit modeling of structure but lack desirable properties such as graph well-formedness guarantees or built-in graph-sentence alignments. In this work we explore the integration of general pre-trained sequence-to-sequence language models and a structure-aware transition-based approach. We depart from a pointer-based transition system and propose a simplified transition set, designed to better exploit pre-trained language models for structured fine-tuning. We also explore modeling the parser state within the pre-trained encoder-decoder architecture and different vocabulary strategies for the same purpose. We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2.0.
Program Repair with Repeated Learning
Authors: Anonymous
Date: 2021-04-24 00:00:00
Tasks: Program Repair

A key challenge in generate-and-validate automated program repair is directing the search for fixes so that it can efficiently find those that are more likely to be correct. To this end, several techniques use machine learning to capture the features of programmer-written fixes. In existing approaches, fitting the model typically takes place before fix generation and independent of it: the fix generation process uses the learned model as one of its inputs. However, the intermediate outcomes of an ongoing fix generation process often provide valuable information about which fixes were "better"; this information could profitably be used to retrain the model, so that each new iteration of the fixing process would also learn from the outcome of previous ones. In this paper, we propose the Liana technique for automated program repair, which is based on this idea of repeatedly learning the features of generated fixes. To this end, Liana uses a fine-grained model that combines information about fix characteristics, their relations to the fixing context, and the result of test executions. The model is initially trained offline, and then repeatedly updated online as the fix generation process unravels; at any step, the most up to date model is used to guide the search for fixes—prioritizing those that are more likely to include the right ingredients. In an experimental evaluation on 732 real-world Java bugs from 3 popular benchmarks, Liana built correct fixes for 134 faults (83 ranked as first in its output)—improving over several other generate-and-validate program repair tools according to various measures.
Markov Chain Monte Carlo Policy Optimization
Authors: Daniel Hsu
Date: 2021-01-04 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning (RL), Variational Inference

Discovering approximately optimal policies in domains is crucial to applying reinforcement learning (RL) in many real-world scenarios, which is termed as policy optimization. By viewing the policy optimization from the perspective of variational inference, the representation power of policy network allows us to obtain the approximate posterior of actions conditioned on the states, with the entropy or KL regularization. However, in practice the policy optimization may lead to suboptimal policy estimates due to amortization gap. Inspired by the Markov Chain Monte Carlo (MCMC) techniques, instead of optimizing policy parameters or policy distributions directly, we propose a new policy optimization method, incorporating gradient-based feedback in various ways. The empirical evaluation verifies the performance improvement of the proposed method in many continuous control benchmarks.
Hamiltonian Policy Optimization in Reinforcement Learning
Authors: Derek Hsu
Date: 2021-03-23 00:00:00
Tasks: continuous-control, Continuous Control, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

Approximating optimal policies in reinforcement learning (RL) is often necessary in many real-world scenarios, which is termed as policy optimization. By viewing the reinforcement learning from the perspective of variational inference (VI), the policy network is trained to obtain the approximate posterior of actions given the optimality criteria.  
However, in practice, the policy optimization may lead to suboptimal policy estimates due to the amortization gap and insufficient exploration. In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate policy optimization with HMC. As such we choose evolving actions from the base policy according to HMC. First, HMC can improve the policy distribution to better approximate the posterior and hence reduces the amortization gap. Second, HMC can also guide the exploration more to the regions with higher action values, enhancing the exploration efficiency. Instead of directly applying HMC into RL, we propose a new leapfrog operator to simulate the Hamiltonian dynamics.  
With comprehensive empirical experiments on continuous control baselines, including MuJoCo, PyBullet Roboschool and DeepMind Control Suite, we show that the proposed approach is a data-efficient, and an easy-to-implement improvement over previous policy optimization methods. Besides, the proposed approach can also outperform previous methods on DeepMind Control Suite, which has image-based high-dimensional observation space.
Exploring the Inner Life of Neural Networks with Robust Rules
Authors: Anonymous
Date: 2021-03-23 00:00:00
Tasks: Descriptive

We propose a novel method for exploring how neurons within neural networks interact. In particular, we consider activation values of a network for given data, and propose to mine noise-robust rules of the form X → Y , where X and Y are sets of neurons in different layers. We identify the best set of rules by the Minimum Description Length Principle as the rules that together are most descriptive of the activation data.To learn good rule sets in practice, we propose the unsupervised ExplaiNN algorithm. Extensive evaluation shows that the patterns it discovers give clear insight in how networks perceive the world: they identify shared, respectively class-specific traits, compositionality within the network, as well as locality in convolutional layers. Moreover, these patterns are not only easily interpretable, but also supercharge prototyping as they identify which groups of neurons to consider in unison.
Hamiltonian Policy Optimization
Authors: Derek Hsu (a.k.a. Duo Xu)
Date: 2021-02-28 00:00:00
Tasks: continuous-control, Continuous Control, MuJoCo, reinforcement-learning, Reinforcement Learning (RL), Variational Inference

Approximating optimal policies in reinforcement learning (RL) is often necessary in many real-world scenarios, which is termed as policy optimization. By viewing the reinforcement learning from the perspective of variational inference (VI), the policy network is trained to obtain the approximate posterior of actions given the optimality criteria.  
However, in practice, the policy optimization may lead to suboptimal policy estimates due to the amortization gap and insufficient exploration. In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate policy optimization with HMC. As such we choose evolving actions from the base policy according to HMC. First, HMC can improve the policy distribution to better approximate the posterior and hence reduces the amortization gap. Second, HMC can also guide the exploration more to the regions with higher action values, enhancing the exploration efficiency. Instead of directly applying HMC into RL, we propose a new leapfrog operator to simulate the Hamiltonian dynamics.  
With comprehensive empirical experiments on continuous control baselines, including MuJoCo, PyBullet Roboschool and DeepMind Control Suite, we show that the proposed approach is a data-efficient, and an easy-to-implement improvement over previous policy optimization methods. Besides, the proposed approach can also outperform previous methods on DeepMind Control Suite, which has image-based high-dimensional observation space.