Multi-Task Video Captioning with Video and Entailment Generation

Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailed caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.Comment: ACL 2017 (14 pages w/ supplementary

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

Semantic Tagging with Deep Residual Networks

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Explicit Contextual Semantics for Text Comprehension

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Relation Classification with Limited Supervision

Large reams of unstructured data, for instance in form textual document collections containing entities and relations, exist in many domains. The process of deriving valuable domain insights and intelligence from such documents collections usually involves the extraction of information such as the relations between the entities in such collections. Relation classification is the task of detecting relations between entities. Supervised machine learning models, which have become the tool of choice for relation classification, require substantial quantities of annotated data for each relation in order to perform optimally. For many domains, such quantities of annotated data for relations may not be readily available, and manually curating such annotations may not be practical due to time and cost constraints. In this work, we develop both model-specific and model-agnostic approaches for relation classification with limited supervision. We start by proposing an approach for learning embeddings for contextual surface patterns, which are the set of surface patterns associated with entity pairs across a text corpus, to provide additional supervision signals for relation classification with limited supervision. We find that this approach improves classification performance on relations with limited supervision instances. However, this initial approach assumes the availability of at least one annotated instance per relation during training. In order to address this limitation, we propose an approach which formulates the task of relation classification as that of textual entailment. This reformulation allows us to use the textual descriptions of relations to classify their instances. It also allows us to utilize existing textual entailment datasets and models to classify relations with zero supervision instances. The two methods proposed previously rely on the use of specific model architectures for relation classification. Since a wide variety of models have been proposed for relation classification in the literature, a more general approach is thus desirable. We subsequently propose our first model-agnostic meta-learning algorithm for relation classification with limited supervision. This algorithm is applicable to any gradient-optimized relation classification model. We show that the proposed approach improves the predictive performance of two existing relation classification models when supervision for relations is limited. Next, because all the approaches we have proposed so far assume the availability of all supervision needed for classifying relations prior to model training, they are unable to handle the case when new supervision for relations becomes available after training. Such new supervision may need to be incorporated into the model to enable it classify new relations or to improve its performance on existing relations. Our last approach addresses this short-coming. We propose a model-agnostic algorithm which enables relation classification models to learn continually from new supervision as it becomes available, while doing so in a data-efficient manner and without forgetting knowledge of previous relations