Zero-Shot Information Extraction as a Unified Text-to-Triple Translation

We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic translation by leveraging the latent knowledge that a pre-trained language model has about the task. We further demonstrate that a simple pre-training task of predicting which relational information corresponds to which input text is an effective way to produce task-specific outputs. This enables the zero-shot transfer of our framework to downstream tasks. We study the zero-shot performance of this framework on open information extraction (OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and factual probe (Google-RE and T-REx). The model transfers non-trivially to most tasks and is often competitive with a fully supervised method without the need for any task-specific training. For instance, we significantly outperform the F1 score of the supervised open information extraction without needing to use its training set.

Extend, don't rebuild: Phrasing conditional graph modification as autoregressive sequence labelling

Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction. A recent work used this technique for modifying scene graphs (He et al. 2020), by first encoding the original graph and then generating the modified one based on this encoding. In this work, we show that we can considerably increase performance on this problem by phrasing it as graph extension instead of graph generation. We propose the first model for the resulting graph extension problem based on autoregressive sequence labelling. On three scene graph modification data sets, this formulation leads to improvements in accuracy over the state-of-the-art between 13 and 24 percentage points. Furthermore, we introduce a novel data set from the biomedical domain which has much larger linguistic variability and more complex graphs than the scene graph modification data sets. For this data set, the state-of-the art fails to generalize, while our model can produce meaningful predictions.

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. High-quality CPC models can significantly benefit applications such as comparative question answering and review-based recommendation. Among the existing approaches, non-deep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We propose Sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC accuracy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.

Data augmentation for low-resource grapheme-to-phoneme mapping

In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context. The basic idea is to start from a baseline system and focus all efforts on data augmentation. We will see that some techniques work, but others do not.

Paradigm Clustering with Weighted Edit Distance

This paper describes our system for the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering, which asks participants to group inflected forms together according their underlying lemma without the aid of annotated training data. We employ agglomerative clustering to group word forms together using a metric that combines an orthographic distance and a semantic distance from word embeddings. We experiment with two variations of an edit distance-based model for quantifying orthographic distance, but, due to time constraints, our system does not improve over the shared task's baseline system.

The structure of online social networks modulates the rate of lexical change

New words are regularly introduced to communities, yet not all of these words persist in a community's lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. Using Poisson regression and survival analysis, our study demonstrates that the community's network structure plays a significant role in lexical change. Apart from overall size, properties including dense connections, the lack of local clusters, and more external contacts promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical leveling despite increased contact but accommodate more niche words. Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.

Studying And Analyzing the modulators and demodulators of PCM

Modulating signals of transmission stations to reception stations is a key factor to guarantee the best possible transmission and reception of these signals .Digital modulation represents a huge evolution in communication field and modulation, which used to depend on analog signal modulation of one parameter-Amplitude . frequency or phase. Digital modulation depends on transforming the transmitted data signal (Bits) and then sending it as samples, and changed back into an analog signals in reception station . In digital systems, digital data are transformed into analog data in the transmitter and does the reverse in the receiver. In digital transmission, on the other hand, as in wired local area networks (WLAN), Digital data are transmitted in their digital state.

Study and analysis of error performance when using multiple pulse position modulation (PPM) over optical fiber

This paper is concerned with an original analysis of an multiple pulse position modulation (MPPM) scheme using graded-index plastic optical fiber.

Development electrical drive Vector control system for three phase induction motor Crane Wire, according to the principle indirect rotor field orientation

In this paper, the development of the drive system of the motor Wire port Crane according to the principle of the indirect field orientation. Require the operation of the port Crane operations, different operation situations of variable speed and Torque with time, which is a case dynamic electro and electro-magnetic transient, may affect the performance of operations, so therefore been building motor Wire port Crane operations system corps in winches achieve: - High performance and Efficiency. - Build the desired control system according to the principle of the Pulse Width Modulation (PWM) and the main power system, taking into account the reduction of changes DC-Link voltage and achieve maximum flux before starting the motor. - Reduction of excess Voltage that may appear on a DC-Link, using the brake Transistor with Brake resisters. At the end of the research has been reviewed and the search results that indicate the outstanding performance of the proposed system with the ability to use the same system to run automated processes different.

سلطة الإدارة في تعديل العقد الإداري

The administration enjoys discretionary powers in the implementation of its decisions to manage the public utility. These discretionary powers include, but not limited to its power to amend the administrative contract when it considers it is necessary to achieve the objectives of the public utility. Therefore, this discretionary power gives the administration the right, in its sole discretion, to make an amendment to the administrative contract, either upward or downward, and in all contracts concluded by the administration, not limiting its powers like this unless there is a misuse of power.