In the e-commerce domain, the accurate extraction of attribute-value pairs (e.g., B rand : Apple ) from product titles and user search queries is crucial for enhancing search and recommendation systems. A major challenge with neural models for this task is the lack of high-quality training data, as the annotations for attribute-value pairs in the available datasets are often incomplete. To address this, we introduce G en T o C, a model designed for training directly with partially-labeled data, eliminating the necessity for a fully annotated dataset. G en T o C employs a marker-augmented generative model to identify potential attributes, followed by a token classification model that determines the associated values for each attribute. G en T o C outperforms existing state-of-the-art models, exhibiting upto 56.3% increase in the number of accurate extractions. Furthermore, we utilize G en T o C to regenerate the training dataset to expand attribute-value annotations. This bootstrapping substantially improves the data quality for training other standard NER models, which are typically faster but less capable in handling partially-labeled data, enabling them to achieve comparable performance to G en T o C. Our results demonstrate G en T o C's unique ability to learn from a limited set of partially-labeled data and improve the training of more efficient models, advancing the automated extraction of attribute-value pairs. Finally, our model has been successfully integrated into IndiaMART, India's largest B2B e-commerce platform, achieving a significant increase of 20.2% in the number of correctly identified attribute-value pairs over the existing deployed system while achieving a high precision of 89.5%. We have released the code for G en T o C model at https://github.com/KnowDisAI/GenToC.

Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages.In this paper, we explore techniques to automatically convert English text for training OpenIE systems in other languages.We introduce the Alignment-Augmented Consistent Translation (AACTRANS) model to translate English sentences and their corresponding extractions consistently with each other -with no changes to vocabulary or semantic meaning which may result from independent translations.Using the data generated with AACTRANS, we train a novel two-stage generative OpenIE model, which we call GEN2OIE, that outputs for each sentence: 1) relations in the first stage and 2) all extractions containing the relation in the second stage.GEN2OIE increases relation coverage using a training data transformation technique that is generalizable to multiple languages, in contrast to existing models that use an English-specific training loss.Evaluations on 5 languages -Spanish, Portuguese, Chinese, Hindi and Telugu -show that the GEN2OIE with AACTRANS data outperforms prior systems by a margin of 6-25% F1. 1

Proper noun compounds, e.g., "Covid vaccine", convey information in a succinct manner (a "Covid vaccine" is a "vaccine that immunizes against the Covid disease").These are commonly used in short-form domains, such as news headlines, but are largely ignored in information-seeking applications.To address this limitation, we release a new manually annotated dataset, PRONCI, consisting of 22.5K proper noun compounds along with their freeform semantic interpretations.PRONCI is 60 times larger than prior noun compound datasets and also includes non-compositional examples, which have not been previously explored.We experiment with various neural models for automatically generating the semantic interpretations from proper noun compounds, ranging from few-shot prompting to supervised learning, with varying degrees of knowledge about the constituent nouns.We find that adding targeted knowledge, particularly about the common noun, results in performance gains of upto 2.8%.Finally, we integrate our model generated interpretations with an existing Open IE system and observe an 7.5% increase in yield at a precision of 85%.The dataset and code are available at https://github.com/dair-iitd/pronci.

While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task.Our work builds upon CopyAttention, a sequence generation OpenIE model (Cui et al., 2018).Our analysis reveals that CopyAttention produces a constant number of extractions per sentence, and its extracted tuples often express redundant information.We present IMOJIE, an extension to Copy-Attention, which produces the next extraction conditioned on all previously extracted tuples.This approach overcomes both shortcomings of CopyAttention, resulting in a variable number of diverse extractions per sentence.We train IMOJIE on training data bootstrapped from extractions of several non-neural systems, which have been automatically filtered to reduce redundancy and noise.IMOJIE outperforms CopyAttention by about 18 F1 pts, and a BERT-based strong baseline by 2 F1 pts, establishing a new state of the art for the task.

A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs.This comes at a significant computational cost.On the other hand, sequence labeling approaches for OpenIE are much faster, but worse in extraction quality.In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10× faster.This is achieved through a novel Iterative Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling task.We improve its performance further by applying coverage (soft) constraints on the grid at training time.Moreover, on observing that the best OpenIE systems falter at handling coordination structures, our OpenIE system also incorporates a new coordination analyzer built with the same IGL architecture.This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12.3 pts improvement in F1 over previous analyzers.Our OpenIE system, OpenIE6 1 , beats the previous systems by as much as 4 pts in F1, while being much faster.