Constrained Conditional Model: Fundamentals and Applications

Chapter 1: Constrained conditional model

Adding declarative constraints to the learning of conditional (probabilistic or discriminative) models is the basis of a machine learning and inference framework known as a constrained conditional model (CCM). To better incorporate expressive prior knowledge into the model and to bias the assignments made by the learned model to satisfy these constraints, the constraint can be used. While keeping training and inference modular and manageable, the framework can be used to support decisions in an expressive output space.

The field of natural language processing (NLP) has recently shown a great deal of interest in such models. Multiple benefits can be gained by posing issues as constrained optimization problems over the results of trained models. By letting you include domain-specific knowledge as global constraints in a first-order language, it frees you up to concentrate on problem modeling. The domain-specific properties of the problem are captured, and precise inference is guaranteed, all within a declarative framework that releases the developer from low-level feature engineering. From a machine learning standpoint, it permits unbundling model generation (learning) from constrained inference, which helps to streamline the former while enhancing the latter in terms of solution quality. When generating compressed sentences, for instance, constraints can be used to guarantee that if a modifier is kept in the compressed sentence, its subject will also be kept. This is in contrast to simply relying on a language model to retain the most commonly used n-grams in the sentence.

The expressive dependency structure can influence or even dictate what assignments are possible when making decisions in many domains (including natural language processing and computer vision problems). Not only are these parameters useful for structured learning tasks like semantic role labeling, but they can also be used for tasks like summarization, textual entailment, and question answering, which draw on a combination of previously learned components. In all these situations, the decision problem can be formulated as a constrained optimization problem with a learned-model objective function and domain/problem-specific constraints.

To aid in making decisions in an expressive output space while keeping training and inference modular and manageable, a learning and inference framework known as constrained conditional models combines the learning of conditional (probabilistic or discriminative) models with declarative constraints (written, for example, using a first-order representation). Hard constraints forbid certain assignments entirely, while soft constraints penalize highly improbable ones. Following, Integer Linear Programming (ILP) was used as the inference framework in most NLP applications, though other algorithms can serve the same purpose.

Given a set of feature functions \{\phi _{i}(x,y)\} and a set of constraints \{C_{i}(x,y)\} , defined over an input structure x\in X and an output structure y\in Y , Two weight vectors are what define a constraint-conditioned model, w and \rho , and is understood to be the answer to the subsequent optimization issue:

.

Each constraint C_{i}\in C is a boolean mapping indicating if the joint assignment (x,y) violates a constraint, and \rho is the penalty incurred for violating the constraints.

Hard constraints are those that have a penalty of infinity, and stand for optimization-related tasks that cannot be accomplished.

Several methods exist for learning and decomposing the objective function used by CCMs, training the model together with its constraints from scratch to splitting up the training and inference phases.

For the latter scenario,, When making a decision, a global decision process takes into account the interdependency of several learned local models.

Which studies compare the benefits of the two training modalities: 1. L+I (learning+inference) models at the neighborhood level, and 2. IBT (global) models (Inference based training), joint training (IBT) is optimal in the limit, as demonstrated both theoretically and experimentally, under certain (generally, good components) L+I can generalize better.

When joint learning is computationally intractable or when training data is not available for joint learning, CCM's ability to combine local models is especially helpful. CCM's adaptability sets it apart from learning frameworks like Markov logic network and others that emphasize joint training and combine statistical data with declarative constraints.

With CCM, domain knowledge (expressed as constraints) can be used to drive learning, allowing for less reliance on human supervision. Places like these were used for research. These works present semi-supervised Constraints Driven Learning (CODL) and demonstrate how adding domain knowledge greatly boosts the performance of the learned model.

Latent learning frameworks, in which the learning problem is defined over a latent representation layer, have also made use of CCMs. Due to the fuzziness of the term correct representation, there is no benchmark labeled data available to the learner to guide their decision. The process of determining the best possible learning representation is modeled as a CCM because it is thought of as a structured prediction process. Several papers addressed this issue, both unsupervised and under supervision. Constraints, which explicitly model the dependencies between representation decisions, have been shown to improve performance across the board.

Natural language processing tasks, such as semantic role labeling, have been formulated within the CCM declarative formulation because of its advantages and the availability of off-the-shelf solvers, allowing for large-scale problems to be efficiently solved.

The main benefit of employing an ILP solver in order to resolve the optimization problem defined by a constrained conditional model is that the ILP solver can take as input a declarative formulation consisting of a linear objective function and a set of linear constraints.

Tutorial on Constrained Conditional Models for Predicting NLP Structures and Integer Linear Programming in NLP

{End Chapter 1}

Chapter 2: Machine learning

Machine learning (ML) is a subfield of computer science that focuses on the study and development of techniques that enable computers to learn, or more specifically, techniques that make use of data in order to enhance a computer's performance on a certain set of tasks.

Machine learning, when used to the solution of business challenges, is also known as predictive analytics.

Learning algorithms are based on the hypothesis that methods, algorithms, and judgments that were successful in the past are likely to continue to be successful in the future as well. These deductions might sometimes be self-evident, such as when one says that "because the sun has risen every morning for the last 10,000 days, there is a good