Semi-tied Units for Efficient Gating in LSTM and Highway Networks

Gating is a key technique used for integrating information from multiple sources by long short-term memory (LSTM) models and has recently also been applied to other models such as the highway network. Although gating is powerful, it is rather expensive in terms of both computation and storage as each gating unit uses a separate full weight matrix. This issue can be severe since several gates can be used together in e.g. an LSTM cell. This paper proposes a semi-tied unit (STU) approach to solve this efficiency issue, which uses one shared weight matrix to replace those in all the units in the same layer. The approach is termed "semi-tied" since extra parameters are used to separately scale each of the shared output values. These extra scaling factors are associated with the network activation functions and result in the use of parameterised sigmoid, hyperbolic tangent, and rectified linear unit functions. Speech recognition experiments using British English multi-genre broadcast data showed that using STUs can reduce the calculation and storage cost by a factor of three for highway networks and four for LSTMs, while giving similar word error rates to the original models.Comment: To appear in Proc. INTERSPEECH 2018, September 2-6, 2018, Hyderabad, Indi

딥 뉴럴 네트워크 기반의 문장 인코더를 이용한 문장 간 관계 모델링

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 이상구.문장 매칭이란 두 문장 간 의미적으로 일치하는 정도를 예측하는 문제이다. 어떤 모델이 두 문장 사이의 관계를 효과적으로 밝혀내기 위해서는 높은 수준의 자연어 텍스트 이해 능력이 필요하기 때문에, 문장 매칭은 다양한 자연어 처리 응용의 성능에 중요한 영향을 미친다. 본 학위 논문에서는 문장 인코더, 매칭 함수, 준지도 학습이라는 세 가지 측면에서 문장 매칭의 성능 개선을 모색한다. 문장 인코더란 문장으로부터 유용한 특질들을 추출하는 역할을 하는 구성 요소로, 본 논문에서는 문장 인코더의 성능 향상을 위하여 Gumbel Tree-LSTM과 Cell-aware Stacked LSTM이라는 두 개의 새로운 아키텍처를 제안한다. Gumbel Tree-LSTM은 재귀적 뉴럴 네트워크(recursive neural network) 구조에 기반한 아키텍처이다. 구조 정보가 포함된 데이터를 입력으로 사용하던 기존의 재귀적 뉴럴 네트워크 모델과 달리, Gumbel Tree-LSTM은 구조가 없는 데이터로부터 특정 문제에 대한 성능을 최대화하는 파싱 전략을 학습한다. Cell-aware Stacked LSTM은 LSTM 구조를 개선한 아키텍처로, 여러 LSTM 레이어를 중첩하여 사용할 때 망각 게이트(forget gate)를 추가적으로 도입하여 수직 방향의 정보 흐름을 더 효율적으로 제어할 수 있도록 한다. 한편, 새로운 매칭 함수로서 우리는 요소별 쌍선형 문장 매칭(element-wise bilinear sentence matching, ElBiS) 함수를 제안한다. ElBiS 알고리즘은 특정 문제를 해결하는 데에 적합한 방식으로 두 문장 표현을 하나의 벡터로 합치는 방법을 자동으로 찾는 것을 목적으로 한다. 문장 표현을 얻을 때에 서로 같은 문장 인코더를 사용한다는 사실로부터 우리는 벡터의 각 요소 간 쌍선형(bilinear) 상호 작용만을 고려하여도 두 문장 벡터 간 비교를 충분히 잘 수행할 수 있다는 가설을 수립하고 이를 실험적으로 검증한다. 상호 작용의 범위를 제한함으로써, 자동으로 유용한 병합 방법을 찾는다는 이점을 유지하면서 모든 상호 작용을 고려하는 쌍선형 풀링 방법에 비해 필요한 파라미터의 수를 크게 줄일 수 있다. 마지막으로, 학습 시 레이블이 있는 데이터와 레이블이 없는 데이터를 함께 사용하는 준지도 학습을 위해 우리는 교차 문장 잠재 변수 모델(cross-sentence latent variable model, CS-LVM)을 제안한다. CS-LVM의 생성 모델은 출처 문장(source sentence)의 잠재 표현 및 출처 문장과 목표 문장(target sentence) 간의 관계를 나타내는 변수로부터 목표 문장이 생성된다고 가정한다. CS-LVM에서는 두 문장이 하나의 모델 안에서 모두 고려되기 때문에, 학습에 사용되는 목적 함수가 더 자연스럽게 정의된다. 또한, 우리는 생성 모델의 파라미터가 더 의미적으로 적합한 문장을 생성하도록 유도하기 위하여 일련의 의미 제약들을 정의한다. 본 학위 논문에서 제안된 개선 방안들은 문장 매칭 과정을 포함하는 다양한 자연어 처리 응용의 효용성을 높일 것으로 기대된다.Sentence matching is a task of predicting the degree of match between two sentences. Since high level of understanding natural language text is needed for a model to identify the relationship between two sentences, it is an important component for various natural language processing applications. In this dissertation, we seek for the improvement of the sentence matching module from the following three ingredients: sentence encoder, matching function, and semi-supervised learning. To enhance a sentence encoder network which takes responsibility of extracting useful features from a sentence, we propose two new sentence encoder architectures: Gumbel Tree-LSTM and Cell-aware Stacked LSTM (CAS-LSTM). Gumbel Tree-LSTM is based on a recursive neural network (RvNN) architecture, however unlike typical RvNN architectures it does not need a structured input. Instead, it learns from data a parsing strategy that is optimized for a specific task. The latter, CAS-LSTM, extends the stacked long short-term memory (LSTM) architecture by introducing an additional forget gate for better handling of vertical information flow. And then, as a new matching function, we present the element-wise bilinear sentence matching (ElBiS) function. It aims to automatically find an aggregation scheme that fuses two sentence representations into a single one suitable for a specific task. From the fact that a sentence encoder is shared across inputs, we hypothesize and empirically prove that considering only the element-wise bilinear interaction is sufficient for comparing two sentence vectors. By restricting the interaction, we can largely reduce the number of required parameters compared with full bilinear pooling methods without losing the advantage of automatically discovering useful aggregation schemes. Finally, to facilitate semi-supervised training, i.e. to make use of both labeled and unlabeled data in training, we propose the cross-sentence latent variable model (CS-LVM). Its generative model assumes that a target sentence is generated from the latent representation of a source sentence and the variable indicating the relationship between the source and the target sentence. As it considers the two sentences in a pair together in a single model, the training objectives are defined more naturally than prior approaches based on the variational auto-encoder (VAE). We also define semantic constraints that force the generator to generate semantically more plausible sentences. We believe that the improvements proposed in this dissertation would advance the effectiveness of various natural language processing applications containing modeling sentence pairs.Chapter 1 Introduction 1 1.1 Sentence Matching 1 1.2 Deep Neural Networks for Sentence Matching 2 1.3 Scope of the Dissertation 4 Chapter 2 Background and Related Work 9 2.1 Sentence Encoders 9 2.2 Matching Functions 11 2.3 Semi-Supervised Training 13 Chapter 3 Sentence Encoder: Gumbel Tree-LSTM 15 3.1 Motivation 15 3.2 Preliminaries 16 3.2.1 Recursive Neural Networks 16 3.2.2 Training RvNNs without Tree Information 17 3.3 Model Description 19 3.3.1 Tree-LSTM 19 3.3.2 Gumbel-Softmax 20 3.3.3 Gumbel Tree-LSTM 22 3.4 Implementation Details 25 3.5 Experiments 27 3.5.1 Natural Language Inference 27 3.5.2 Sentiment Analysis 32 3.5.3 Qualitative Analysis 33 3.6 Summary 36 Chapter 4 Sentence Encoder: Cell-aware Stacked LSTM 38 4.1 Motivation 38 4.2 Related Work 40 4.3 Model Description 43 4.3.1 Stacked LSTMs 43 4.3.2 Cell-aware Stacked LSTMs 44 4.3.3 Sentence Encoders 46 4.4 Experiments 47 4.4.1 Natural Language Inference 47 4.4.2 Paraphrase Identification 50 4.4.3 Sentiment Classification 52 4.4.4 Machine Translation 53 4.4.5 Forget Gate Analysis 55 4.4.6 Model Variations 56 4.5 Summary 59 Chapter 5 Matching Function: Element-wise Bilinear Sentence Matching 60 5.1 Motivation 60 5.2 Proposed Method: ElBiS 61 5.3 Experiments 63 5.3.1 Natural language inference 64 5.3.2 Paraphrase Identification 66 5.4 Summary and Discussion 68 Chapter 6 Semi-Supervised Training: Cross-Sentence Latent Variable Model 70 6.1 Motivation 70 6.2 Preliminaries 71 6.2.1 Variational Auto-Encoders 71 6.2.2 von Mises–Fisher Distribution 73 6.3 Proposed Framework: CS-LVM 74 6.3.1 Cross-Sentence Latent Variable Model 75 6.3.2 Architecture 78 6.3.3 Optimization 79 6.4 Experiments 84 6.4.1 Natural Language Inference 84 6.4.2 Paraphrase Identification 85 6.4.3 Ablation Study 86 6.4.4 Generated Sentences 88 6.4.5 Implementation Details 89 6.5 Summary and Discussion 90 Chapter 7 Conclusion 92 Appendix A Appendix 96 A.1 Sentences Generated from CS-LVM 96Docto

Evolution of A Common Vector Space Approach to Multi-Modal Problems

A set of methods to address computer vision problems has been developed. Video un- derstanding is an activate area of research in recent years. If one can accurately identify salient objects in a video sequence, these components can be used in information retrieval and scene analysis. This research started with the development of a course-to-fine frame- work to extract salient objects in video sequences. Previous work on image and video frame background modeling involved methods that ranged from simple and efficient to accurate but computationally complex. It will be shown in this research that the novel approach to implement object extraction is efficient and effective that outperforms the existing state-of-the-art methods. However, the drawback to this method is the inability to deal with non-rigid motion. With the rapid development of artificial neural networks, deep learning approaches are explored as a solution to computer vision problems in general. Focusing on image and text, the image (or video frame) understanding can be achieved using CVS. With this concept, modality generation and other relevant applications such as automatic im- age description, text paraphrasing, can be explored. Specifically, video sequences can be modeled by Recurrent Neural Networks (RNN), the greater depth of the RNN leads to smaller error, but that makes the gradient in the network unstable during training.To overcome this problem, a Batch-Normalized Recurrent Highway Network (BNRHN) was developed and tested on the image captioning (image-to-text) task. In BNRHN, the highway layers are incorporated with batch normalization which diminish the gradient vanishing and exploding problem. In addition, a sentence to vector encoding framework that is suitable for advanced natural language processing is developed. This semantic text embedding makes use of the encoder-decoder model which is trained on sentence paraphrase pairs (text-to-text). With this scheme, the latent representation of the text is shown to encode sentences with common semantic information with similar vector rep- resentations. In addition to image-to-text and text-to-text, an image generation model is developed to generate image from text (text-to-image) or another image (image-to- image) based on the semantics of the content. The developed model, which refers to the Multi-Modal Vector Representation (MMVR), builds and encodes different modalities into a common vector space that achieve the goal of keeping semantics and conversion between text and image bidirectional. The concept of CVS is introduced in this research to deal with multi-modal conversion problems. In theory, this method works not only on text and image, but also can be generalized to other modalities, such as video and audio. The characteristics and performance are supported by both theoretical analysis and experimental results. Interestingly, the MMVR model is one of the many possible ways to build CVS. In the final stages of this research, a simple and straightforward framework to build CVS, which is considered as an alternative to the MMVR model, is presented

A comparison among deep learning techniques in an autonomous driving context

Al giorno d’oggi, l’intelligenza artificiale è uno dei campi di ricerca che sta ricevendo sempre più attenzioni. Il miglioramento della potenza computazionale a disposizione dei ricercatori e sviluppatori sta rinvigorendo tutto il potenziale che era stato espresso a livello teorico agli albori dell’Intelligenza Artificiale. Tra tutti i campi dell’Intelligenza Artificiale, quella che sta attualmente suscitando maggiore interesse è la guida autonoma. Tantissime case automobilistiche e i più illustri college americani stanno investendo sempre più risorse su questa tecnologia. La ricerca e la descrizione dell’ampio spettro delle tecnologie disponibili per la guida autonoma è parte del confronto svolto in questo elaborato. Il caso di studio si incentra su un’azienda che partendo da zero, vorrebbe elaborare un sistema di guida autonoma senza dati, in breve tempo ed utilizzando solo sensori fatti da loro. Partendo da reti neurali e algoritmi classici, si è arrivati ad utilizzare algoritmi come A3C per descrivere tutte l’ampio spettro di possibilità. Le tecnologie selezionate verranno confrontate in due esperimenti. Il primo è un esperimento di pura visione artificiale usando DeepTesla. In questo esperimento verranno confrontate tecnologie quali le tradizionali tecniche di visione artificiale, CNN e CNN combinate con LSTM. Obiettivo è identificare quale algoritmo ha performance migliori elaborando solo immagini. Il secondo è un esperimento su CARLA, un simulatore basato su Unreal Engine. In questo esperimento, i risultati ottenuti in ambiente simulato con CNN combinate con LSTM, verranno confrontati con i risultati ottenuti con A3C. Obiettivo sarà capire se queste tecniche sono in grado di muoversi in autonomia utilizzando i dati forniti dal simulatore. Il confronto mira ad identificare le criticità e i possibili miglioramenti futuri di ciascuno degli algoritmi proposti in modo da poter trovare una soluzione fattibile che porta ottimi risultati in tempi brevi

Multimodal Assessment of Cognitive Decline: Applications in Alzheimer’s Disease and Depression

The initial diagnosis and assessment of cognitive decline are generally based around the judgement of clinicians, and commonly used semi-structured interviews, guided by pre-determined sets of topics, in a clinical set-up. Publicly available multimodal datasets have provided an opportunity to explore a range of experiments in the automatic detecting of cognitive decline. Drawing on the latest developments in representation learning, machine learning, and natural language processing, we seek to develop models capable of identifying cognitive decline with an eye to discovering the differences and commonalities that should be considered in computational treatment of mental health disorders. We present models that learn the indicators of cognitive decline from audio and visual modalities as well as lexical, syntactic, disfluency and pause information. Our study is carried out in two parts: moderation analysis and predictive modelling. We do some experiments with different fusion techniques. Our approaches are motivated by some of the recent efforts in multimodal fusion for classifying cognitive states to capture the interaction between modalities and maximise the use and combination of each modality. We create tools for detecting cognitive decline and use them to analyze three major datasets containing speech produced by people with and without cognitive decline. These findings are being used to develop multimodal models for the detection of depression and Alzheimer’s dementia

Syntactic inductive biases for deep learning methods

Le débat entre connexionnisme et symbolisme est l'une des forces majeures qui animent le développement de l'Intelligence Artificielle. L'apprentissage profond et la linguistique théorique sont les domaines d'études les plus représentatifs pour les deux écoles respectivement. Alors que la méthode d'apprentissage profond a fait des percées impressionnantes et est devenue la principale raison de la récente prospérité de l'IA pour l'industrie et les universités, la linguistique et le symbolisme occupent quelque domaines importantes, notamment l'interprétabilité et la fiabilité. Dans cette thèse, nous essayons de construire une connexion entre les deux écoles en introduisant des biais inductifs linguistiques pour les modèles d'apprentissage profond. Nous proposons deux familles de biais inductifs, une pour la structure de circonscription et une autre pour la structure de dépendance. Le biais inductif de circonscription encourage les modèles d'apprentissage profond à utiliser différentes unités (ou neurones) pour traiter séparément les informations à long terme et à court terme. Cette séparation fournit un moyen pour les modèles d'apprentissage profond de construire les représentations hiérarchiques latentes à partir d'entrées séquentielles, dont une représentation de niveau supérieur est composée et peut être décomposée en une série de représentations de niveau inférieur. Par exemple, sans connaître la structure de vérité fondamentale, notre modèle proposé apprend à traiter l'expression logique en composant des représentations de variables et d'opérateurs en représentations d'expressions selon sa structure syntaxique. D'autre part, le biais inductif de dépendance encourage les modèles à trouver les relations latentes entre les mots dans la séquence d'entrée. Pour le langage naturel, les relations latentes sont généralement modélisées sous la forme d'un graphe de dépendance orienté, où un mot a exactement un nœud parent et zéro ou plusieurs nœuds enfants. Après avoir appliqué cette contrainte à un modèle de type transformateur, nous constatons que le modèle est capable d'induire des graphes orientés proches des annotations d'experts humains, et qu'il surpasse également le modèle de transformateur standard sur différentes tâches. Nous pensons que ces résultats expérimentaux démontrent une alternative intéressante pour le développement futur de modèles d'apprentissage profond.The debate between connectionism and symbolism is one of the major forces that drive the development of Artificial Intelligence. Deep Learning and theoretical linguistics are the most representative fields of study for the two schools respectively. While the deep learning method has made impressive breakthroughs and became the major reason behind the recent AI prosperity for industry and academia, linguistics and symbolism still holding some important grounds including reasoning, interpretability and reliability. In this thesis, we try to build a connection between the two schools by introducing syntactic inductive biases for deep learning models. We propose two families of inductive biases, one for constituency structure and another one for dependency structure. The constituency inductive bias encourages deep learning models to use different units (or neurons) to separately process long-term and short-term information. This separation provides a way for deep learning models to build the latent hierarchical representations from sequential inputs, that a higher-level representation is composed of and can be decomposed into a series of lower-level representations. For example, without knowing the ground-truth structure, our proposed model learns to process logical expression through composing representations of variables and operators into representations of expressions according to its syntactic structure. On the other hand, the dependency inductive bias encourages models to find the latent relations between entities in the input sequence. For natural language, the latent relations are usually modeled as a directed dependency graph, where a word has exactly one parent node and zero or several children nodes. After applying this constraint to a transformer-like model, we find the model is capable of inducing directed graphs that are close to human expert annotations, and it also outperforms the standard transformer model on different tasks. We believe that these experimental results demonstrate an interesting alternative for the future development of deep learning models

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201

Continual Learning in Recurrent Neural Networks with Hypernetworks

The last decade has seen a surge of interest in continual learning (CL), and a variety of methods have been developed to alleviate catastrophic forgetting. However, most prior work has focused on tasks with static data, while CL on sequential data has remained largely unexplored. Here we address this gap in two ways. First, we evaluate the performance of established CL methods when applied to recurrent neural networks (RNNs). We primarily focus on elastic weight consolidation, which is limited by a stability-plasticity trade-off, and explore the particularities of this trade-off when using sequential data. We show that high working memory requirements, but not necessarily sequence length, lead to an increased need for stability at the cost of decreased performance on subsequent tasks. Second, to overcome this limitation we employ a recent method based on hypernetworks and apply it to RNNs to address catastrophic forgetting on sequential data. By generating the weights of a main RNN in a task-dependent manner, our approach disentangles stability and plasticity, and outperforms alternative methods in a range of experiments. Overall, our work provides several key insights on the differences between CL in feedforward networks and in RNNs, while offering a novel solution to effectively tackle CL on sequential data.Comment: 13 pages and 4 figures in the main text; 20 pages and 2 figures in the supplementary material