Unsupervised structure induction and multimodal grounding

Structured representations build upon symbolic abstraction (e.g., words in natural language and visual concepts in natural images), offer a principled way of encoding our perceptions about the physical world, and enable the human-like generalization of machine learning systems. The predominant paradigm for learning structured representations of the observed data has been supervised learning, but it is limited in several respects. First, supervised learning is challenging given the scarcity of labeled data. Second, conventional approaches to structured prediction have been relying on a single modality (e.g., either images or text), ignoring the learning cues that may have been specified in and can be readily obtained from other modalities of data. In this thesis, we investigate unsupervised approaches to structure induction in a multimodal setting. Unsupervised learning is inherently difficult in general, let alone inducing complex and discrete structures from data without direct supervision. By considering the multimodal setting, we leverage the alignments between different data modalities (e.g., text, audio, and images) to facilitate the learning of structure-induction models, e.g., knowing that the individual words in ``a white pigeon'' always appear with the same visual object, a language parser is likely to treat them as a whole (i.e., phrase). The multimodal learning setting is practically viable because multimodal alignments are generally abundant. For example, they can be found in online posts such as news and tweets that usually contain images and associated text, and in (YouTube) videos, where audio, scripts, and scenes are synchronized and grounded in each other. We develop structure-induction models, which are capable of exploiting bimodal image-text alignments, for two modalities: (1) for natural language, we consider unsupervised syntactic parsing with phrase-structure grammars and regularize the parser by using visual image groundings; and (2) for visual images, we induce scene graph representations by mapping arguments and predicates in the text to their visual counterparts (i.e., visual objects and relations among them) in an unsupervised manner. While useful, crossmodal alignments are not always abundantly available on the web, e.g., the alignments between non-speech audio and text. We tackle the challenge by sharing the visual modality between image-text alignment and image-audio alignment; images function as a pivot and connect audio and text. The contributions of this thesis span from model development to data collection. We demonstrated the feasibility of applying multimodal learning techniques to unsupervised structure induction and multimodal alignment collection. Our work opens up new avenues for multimodal and unsupervised structured representation learning

Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences

We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a representation of the observable world state. We introduce a multi-level aligner that empowers our model to focus on sentence "regions" salient to the current world state by using multiple abstractions of the input sentence. In contrast to existing methods, our model uses no specialized linguistic resources (e.g., parsers) or task-specific annotations (e.g., seed lexicons). It is therefore generalizable, yet still achieves the best results reported to-date on a benchmark single-sentence dataset and competitive results for the limited-training multi-sentence setting. We analyze our model through a series of ablations that elucidate the contributions of the primary components of our model.Comment: To appear at AAAI 2016 (and an extended version of a NIPS 2015 Multimodal Machine Learning workshop paper

Unsupervised grammar induction with Combinatory Categorial Grammars

Language is a highly structured medium for communication. An idea starts in the speaker's mind (semantics) and is transformed into a well formed, intelligible, sentence via the specific syntactic rules of a language. We aim to discover the fingerprints of this process in the choice and location of words used in the final utterance. What is unclear is how much of this latent process can be discovered from the linguistic signal alone and how much requires shared non-linguistic context, knowledge, or cues. Unsupervised grammar induction is the task of analyzing strings in a language to discover the latent syntactic structure of the language without access to labeled training data. Successes in unsupervised grammar induction shed light on the amount of syntactic structure that is discoverable from raw or part-of-speech tagged text. In this thesis, we present a state-of-the-art grammar induction system based on Combinatory Categorial Grammars. Our choice of syntactic formalism enables the first labeled evaluation of an unsupervised system. This allows us to perform an in-depth analysis of the system’s linguistic strengths and weaknesses. In order to completely eliminate reliance on any supervised systems, we also examine how performance is affected when we use induced word clusters instead of gold-standard POS tags. Finally, we perform a semantic evaluation of induced grammars, providing unique insights into future directions for unsupervised grammar induction systems

Inducing grammars from linguistic universals and realistic amounts of supervision

The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance can be achieved with plentiful supervised data. However, such resources are extremely costly to produce, making them an unlikely option for building NLP tools in under-resourced languages or domains. This dissertation is concerned with reducing the annotation required to learn NLP models, with the goal of opening up the range of domains and languages to which NLP technologies may be applied. In this work, we explore the possibility of learning from a degree of supervision that is at or close to the amount that could reasonably be collected from annotators for a particular domain or language that currently has none. We show that just a small amount of annotation input — even that which can be collected in just a few hours — can provide enormous advantages if we have learning algorithms that can appropriately exploit it. This work presents new algorithms, models, and approaches designed to learn grammatical information from weak supervision. In particular, we look at ways of intersecting a variety of different forms of supervision in complementary ways, thus lowering the overall annotation burden. Sources of information include tag dictionaries, morphological analyzers, constituent bracketings, and partial tree annotations, as well as unannotated corpora. For example, we present algorithms that are able to combine faster-to-obtain type-level annotation with unannotated text to remove the need for slower-to-obtain token-level annotation. Much of this dissertation describes work on Combinatory Categorial Grammar (CCG), a grammatical formalism notable for its use of structured, logic-backed categories that describe how each word and constituent fits into the overall syntax of the sentence. This work shows how linguistic universals intrinsic to the CCG formalism itself can be encoded as Bayesian priors to improve learning.Computer Science

Grounding language in events

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 137-142).Broadcast video and virtual environments are just two of the growing number of domains in which language is embedded in multiple modalities of rich non-linguistic information. Applications for such multimodal domains are often based on traditional natural language processing techniques that ignore the connection between words and the non-linguistic context in which they are used. This thesis describes a methodology for representing these connections in models which ground the meaning of words in representations of events. Incorporating these grounded language models with text-based techniques significantly improves the performance of three multimodal applications: natural language understanding in videogames, sports video search and automatic speech recognition. Two approaches to representing the structure of events are presented and used to model the meaning of words. In the domain of virtual game worlds, a hand-designed hierarchical behavior grammar is used to explicitly represent all the various actions that an agent can take in a virtual world. This grammar is used to interpret events by parsing sequences of observed actions in order to generate hierarchical event structures. In the noisier and more open -ended domain of broadcast sports video, hierarchical temporal patterns are automatically mined from large corpora of unlabeled video data. The structure of events in video is represented by vectors of these hierarchical patterns.(cont.) Grounded language models are encoded using Hierarchical Bayesian models to represent the probability of words given elements of these event structures. These grounded language models are used to incorporate non-linguistic information into text-based approaches to multimodal applications. In the virtual game domain, this non-linguistic information improves natural language understanding for a virtual agent by nearly 10% and cuts in half the negative effects of noise caused by automatic speech recognition. For broadcast video of baseball and American football, video search systems that incorporate grounded language models are shown to perform up to 33% better than text-based systems. Further, systems for recognizing speech in baseball video that use grounded language models show 25% greater word accuracy than traditional systems.by Michael Ben Fleischman.Ph.D

Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning

Task semantics can be expressed by a set of input-to-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning from task instructions. Despite its impressive progress, there are some common issues that the community struggles with. This survey paper tries to summarize the current research on instruction learning, particularly, by answering the following questions: (i) what is task instruction, and what instruction types exist? (ii) how to model instructions? (iii) what factors influence and explain the instructions' performance? (iv) what challenges remain in instruction learning? To our knowledge, this is the first comprehensive survey about textual instructions.Comment: Early Draft. Will be further reorganized and polished. The paper list is available at https://github.com/RenzeLou/awesome-instruction-learnin