Weakly Supervised Learning

Traditionally, word sense disambiguation (WSD) involves a different context model for each individual word. This paper presents a new approach to WSD using weakly supervised learning. Statistical models are not …...

Traditionally, word sense disambiguation (WSD) involves a different context classifi- cation model for each individual word. This paper presents a weakly supervised learning approach to WSD based on learning a …...

In this paper, we present a weakly super- vised learning approach for spoken lan- guage understanding in domain-specific dialogue systems. We model the task of spoken language understanding as a …...

We investigate automatic classification of speculative language (‘hedging’), biomedical text using weakly supervisedin machine learning. Our contributions include a precise description of the task with anno- tation guidelines, analysis and …...

. When Part-of-Speech annotated data is scarce, e.g. for under resourced languages, one can turn to cross- lingual transfer and crawled dictionaries to collect partially supervised data. We cast this …...

We present two Twitter datasets annotated with coarse-grained word senses (super- senses), as well as a series of experiments with three learning scenarios for super- sense tagging: weakly supervised learn- …...

We present a Bayesian formulation for weakly-supervised learning of a Combina- tory Categorial Grammar (CCG) supertag- ger with an HMM. We assume supervi- sion in the form of a tag …...

In this paper, we apply a weakly-supervised learning approach for slot tagging using con- ditional random fields by exploiting web search click logs. We extend the constrained lattice training of …...

AbstractCapabilities of detecting temporal and causal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the …...

AbstractUser generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on …...

AbstractMultiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single …...

AbstractUser-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be …...

AbstractFor machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited …...

AbstractAmbiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer. In this paper, we introduce …...

AbstractImage captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning …...

AbstractWeakly supervised machine reading comprehension (MRC) task is practical and promising for its easily available and massive training data, but inevitablely introduces noise. Existing related methods usually incorporate extra submodels …...

AbstractKnowing whether a published research result can be replicated is important. Carrying out direct replication of published research incurs a high cost. There are efforts tried to use machine learning …...

AbstractIn the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on …...

AbstractNeural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models …...

AbstractMulti-label Text Classification (MLTC) is the task of categorizing documents into one or more topics. Considering the large volumes of data and varying domains of such tasks, fully supervised learning …...

AbstractWeakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study …...

AbstractUtilizing language models (LMs) without internal access is becoming an attractive paradigm in the field of NLP as many cutting-edge LMs are released through APIs and boast a massive scale. …...

AbstractWeakly supervised learning is a popular approach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it allows training models with noisy annotations …...