Abstract

The teacher-student framework aims to improve the sample efficiency of RL algorithms by deploying an advising mechanism in which a teacher helps a student by guiding its exploration. Prior work in this field has considered an advising mechanism where the teacher advises the student about the optimal action to take in a given state. However, real-world teachers can leverage domain expertise to provide more informative signals. Using this insight, we propose to extend the current advising framework wherein the teacher would provide not only the optimal action but also a qualitative assessment of the state. We introduce a novel architecture, namely Advice Replay Memory (ARM), to effectively reuse the advice provided by the teacher. We demonstrate the robustness of our approach by showcasing our experiments on multiple Atari 2600 games using a fixed set of hyper-parameters. Additionally, we show that a student taking help even from a suboptimal teacher can achieve significant performance boosts and eventually outperform the teacher. Our approach outperforms the baselines even when provided with comparatively suboptimal teachers and an advising budget, which is smaller by orders of magnitude. The contributions of our paper are 4-fold (a) supplementing student’s knowledge by providing the state category (b) introduction of ARM to effectively reuse the advice throughout learning (c) ability to achieve significant performance boost even with a coarse state categorization (d) enabling the student to outperform the teacher.

Abstract

In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module’s action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL methods such as deep deterministic policy gradient (DDPG) and deep Q-learning. We provide insights into the convergence properties of GRACIAS and also show that previous MRL algorithms reduce to special cases of our framework. We experimentally demonstrate on several standard MRL domains that our approach works significantly better than the previous MRL methods, and is highly robust to incomparable reward scales. Our framework extends MRL to complex Atari games such as Qbert, and has a better learning curve than the conventional RL algorithms.

Abstract

Virtual Reality has evolved as a powerful, embedded and immersive technology medium to transform dating experiences. However, there is no rigorous CSCW research examining ‘dating’ in VR, despite social interaction being a serious topic of exploration. We aim to push the CSCW discourse on social interaction further by analyzing the dynamics of romantic reciprocality in a fully immersive VR application. Through a qualitative study of 30 participants in 15 pairs, we examine a customizable VR application ‘RecRoom’ as a dating technology medium to analyze how dimensions of interaction - including but not limited to voice, haptics and spatiality - influence dynamics of dating experiences. We employ Tinder as a contrasting chat based medium to situate and deepen our learnings about dating in VR. Our study finds VR allowing users to efficiently and effectively ‘gauge’ matches resulting in well informed decisions to meet (or not) virtual partners ’IRL’ or in real life than existing chat based mediums like Tinder. We believe this leads to improved experience of first dates.

Abstract

This paper introduces a new online learning framework for multiclass classification called learning with diluted bandit feedback. At every time step, the algorithm predicts a candidate label set instead of a single label for the observed example. It then receives feedback from the environment whether the actual label lies in this candidate label set or not. This feedback is called "diluted bandit feedback". Learning in this setting is even more challenging than the bandit feedback setting, as there is more uncertainty in the supervision. We propose an algorithm for multiclass classification using dilute bandit feedback (MC-DBF), which uses the exploration-exploitation strategy to predict the candidate set in each trial. We show that the proposed algorithm achieves O(T^{1-frac{1}{m+2}}) mistake bound if candidate label set size (in each step) is m. We demonstrate the effectiveness of the proposed approach with extensive simulations.

Abstract

The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have become one of the major concerns because of its socio-political consequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal detection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contextual references, and handling multiple aspects of antisemitism. Unfortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab respectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific antisemitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the efficacy of the proposed system. Finally, we also present a qualitative analysis of our study.

Abstract

Tables are widely used in various kinds of documents to present information concisely. Understanding tables is a challenging problem that requires an understanding of language and table structure, along with numerical and logical reasoning. In this paper, we present our systems to solve Task 9 of SemEval-2021: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and a statement, predicting whether the table supports the statement and (B) Predicting which cells in the table provide evidence for/against the statement. We fine-tune TAPAS (a model which extends BERT's architecture to capture tabular structure) for both the subtasks as it has shown state-of-the-art performance in various table understanding tasks. In subtask A, we evaluate how transfer learning and standardizing tables to have a single header row improves TAPAS' performance. In subtask B, we evaluate how different fine-tuning strategies can improve TAPAS' performance. Our systems achieve an F1 score of 67.34 in subtask A three-way classification, 72.89 in subtask A two-way classification, and 62.95 in subtask B.

Abstract

Code-mixing is a common phenomenon in multilingual societies around the world and is especially common in social media texts. Traditional NLP systems, usually trained on monolingual corpora, do not perform well on code-mixed texts. Training specialized models for code-switched texts is difficult due to the lack of large-scale datasets. Translating code-mixed data into standard languages like English could improve performance on various code-mixed tasks since we can use transfer learning from state-of-the-art English models for processing the translated data. This paper focuses on two sequence-level classification tasks for English-Hindi code mixed texts, which are part of the GLUECoS benchmark-Natural Language Inference and Sentiment Analysis. We propose using various pre-trained models that have been fine-tuned for similar English-only tasks and have shown state-of-the-art performance. We further fine-tune these models on the translated code-mixed datasets and achieve state-of-the-art performance in both tasks. To translate English-Hindi code-mixed data to English, we use mBART, a pre-trained multilingual sequence-to-sequence model that has shown competitive performance on various low-resource machine translation pairs and has also shown performance gains in languages that were not in its pre-training corpus.

Abstract

Code-mixed languages are very popular in multilingual societies around the world, yet the resources lag behind to enable robust systems on such languages. A major contributing factor is the informal nature of these languages which makes it difficult to collect code-mixed data. In this paper, we propose our system for Task 1 of CACLS 2021 to generate a machine translation system for English to Hinglish in a supervised setting. Translating in the given direction can help expand the set of resources for several tasks by translating valuable datasets from high resource languages. We propose to use mBART, a pre-trained multilingual sequence-to-sequence model, and fully utilize the pre-training of the model by transliterating the roman Hindi words in the code-mixed sentences to Devanagri script. We evaluate how expanding the input by concatenating Hindi translations of the English sentences improves mBART’s performance. Our system gives a BLEU score of 12.22 on test set. Further, we perform a detailed error analysis of our proposed systems and explore the limitations of the provided dataset and metrics.

Abstract

–Stock rate prediction always attracts investor; it is not only because of interest but also act as a big challenge and a mystery. This problem is very complex in nature and it is quite dynamic type of problem. A lot of volatility always remains and the Global stock market makes it a more challenging and interesting job of stock prediction. Predicting stock prices is a very risky job which may have lot of errors. These errors needs & should be minimized so that financial losses in investment in stock market can be minimized. In this research paper, we have applied supervised machine learning algorithm known as moisture and evaporation decision tree (MEDT). To predict stock prices, we have applied last five years of Historical data with attributes: open high, low and close rates and volume of the particular stock. MEDT algorithm on 5 year’s stock data of (historical prices) has been taken for experiments. As we already know that the market news always affects Stock price movements. The current research we have considered positive or negative news about particular stock. If the news is positive it is called moisture and it assigned a weight between 0 and 1 based on the type and the importance of the news for that particular stock. The news is very impactful then and its moisture value maybe point 0.8, otherwise news is less impactful then its value may vary 0.1 or 0.2. If the news is impactful on the stock rate in the positive direction then its magnitude will be positive moisture value otherwise it will be a negative moisture value. Technique is seen successful in 88% cases.

Abstract

Direct acoustic feature-based speaking rate estimation is useful in applications including pronunciation assessment, dysarthria detection and automatic speech recognition. Most of the existing works on speaking rate estimation have steps which are heuristically designed. In contrast to the existing works, in this work a data-driven approach with convolutional neural network-bidirectional long short-term memory (CNN-BLSTM) network is proposed to jointly optimize all steps in speaking rate estimation through a single framework. Also, unlike existing deep learning-based methods for speaking rate estimation, the proposed approach estimates the speaking rate for an entire speech utterance in one go instead of considering segments of a fixed duration. We consider the traditional 19 sub-band energy (SBE) contours as the low-level features as the input of the proposed CNN-BLSTM network. The state-of-the-art direct acoustic feature-based speaking rate estimation techniques are developed based on 19 SBEs as well. Experiments are performed separately using three native English speech corpora (Switchboard, TIMIT and CTIMIT) and a non-native English speech corpus (ISLE). Among these, TIMIT and Switchboard are used for training the network. However, testing is carried out on all the four corpora as well as TIMIT and Switchboard with additive noise, namely white, car, high-frequency-channel, cockpit, and babble at 20, 10 and 0 dB signal-to-noise ratios. The proposed CNN-BLSTM approach outperforms the best of the existing techniques in clean as well as noisy conditions for all four corpora.