Learning to Generate Questions by Learning What not to Generate

02/27/2019 ∙ by Bang Liu, et al. ∙ 0

Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and provide assessment materials for educational purposes. Existing neural question generation models are not sufficient mainly due to their inability to properly model the process of how each word in the question is selected, i.e., whether repeating the given passage or being generated from a vocabulary. In this paper, we propose our Clue Guided Copy Network for Question Generation (CGC-QG), which is a sequence-to-sequence generative model with copying mechanism, yet employing a variety of novel components and techniques to boost the performance of question generation. In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation. Furthermore, our input passage encoder takes as input, among a diverse range of other features, the prediction made by a clue word predictor, which helps identify whether each word in the input passage is a potential clue to be copied into the target question. The clue word predictor is designed based on a novel application of Graph Convolutional Networks onto a syntactic dependency tree representation of each passage, thus being able to predict clue words only based on their context in the passage and their relative positions to the answer in the tree. We jointly train the clue prediction as well as question generation with multi-task learning and a number of practical strategies to reduce the complexity. Extensive evaluations show that our model significantly improves the performance of question generation and out-performs all previous state-of-the-art neural question generation models by a substantial margin.

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1. Introduction

Asking questions plays a vital role for both the growth of human beings and the improvement of artificial intelligent systems. As a dual task of question answering, question generation based on a text passage and a given answer has attracted much attention in recent years. One of the key applications of question generation is to automatically produce question-answer pairs to enhance machine reading comprehension systems

(Du et al., 2017; Yuan et al., 2017; Tang et al., 2017; Tang et al., 2018). Another application is generating practice exercises and assessments for educational purposes (Heilman and Smith, 2010; Heilman, 2011; Danon and Last, 2017). Besides, question generation is also important in conversational systems and chatbots such as Siri, Cortana, Alexa and Google Assistant, helping them to kick-start and continue a conversation with human users (Mostafazadeh et al., 2016).

Figure 1. Questions are often asked by repeating some text chunks in the input passage, while there is great flexibility as to which chunks are repeated.

Conventional methods for question generation rely on heuristic rules to perform syntactic transformations of a sentence to factual questions

(Heilman, 2011; Chali and Hasan, 2015)

, following grammatical and lexical analysis. However, such methods require specifically crafted transformation and generation rules, with low generalizability. Recently, various neural network models have been proposed for question generation

(Du et al., 2017; Zhou et al., 2017; Hu et al., 2018; Kim et al., 2018; Gao et al., 2018). These models formulate the question generation task as a sequence-to-sequence (Seq2Seq) neural learning problem, where different types of encoders and decoders have been designed. Like many other text generation tasks, the copying or pointer mechanism (Gulcehre et al., 2016) is also widely adopted in question generation to handle the copy phenomenon between input passages and output questions, e.g., (Zhou et al., 2017; Yuan et al., 2017; Subramanian et al., 2017; Kim et al., 2018; Song et al., 2018). However, we point out that a common limitation that hurdles question generation models mainly use copying mechanisms to handle out-of-vocabulary (OOV) issues, and fail to mark a clear boundary between the set of question words that should be directly copied from the input text and those that should be generated instead.

In this paper, we generate questions by learning to identify where each word in the question should come from, i.e., whether generated from a vocabulary or copied from the input text, and given the answer and its context, what words can potentially be copied from the input passage. People usually repeat text chunks in an input passage when asking questions about it, and generate remaining words from their own language to form a complete question. For example, in Fig. 1, given an input passage “Today, Barack Obama gives a speech on democracy in the White House” and an answer “Barack Obama”, we can ask a question “The speech in the White House is given by whom?” Here the text chunks “speech” and “in the White House” are copied from the input passage. However, in the situation that a vocabulary word is an overlap word between a passage and a question, existing models do not clearly identify the underlying reason about the overlapping. In other words, whether this word is copied from the input or generated from vocabulary is not properly labeled. For example, some approaches (Zhou et al., 2017) only take out-of-vocabulary (OOV) words that are shared by both the input passage and target question as copied words, and adopt a copying mechanism to learn to copy these OOV words into questions. In our work, given a passage and a question in a training dataset, we label a word as copied word if it is a nonstop word shared by both the passage and the question, and its word frequency rank in the vocabulary is lower than a threshold. We further aggressively shortlist the output vocabulary based on frequency analysis of the non-copied question words (according to our labeling criteria). Combining this labeling strategy with target vocabulary reduction, during the process of question generation, our model can make better decisions about when to copy or generate, and predict what to generate more accurately.

After applying the above strategies, a remaining problem is which word we should choose to copy. For example, in Fig. 1, we can either ask the question “Who gives a speech today?” or the question “The speech in the White House is given by whom?”, where the two questions are related to two different copied text chunks “today” and “White House”. We can see that asking a question about a passage and a given answer is actually a “one-to-many” mapping problem: we can ask questions in different ways based on which subset of words we choose to copy from input. Therefore, how to enable a neural model to learn what to copy is a critical problem. To solve this issue, we propose to predict the potential clue words in input passages. A word is considered as a “clue word” if it is helpful to reduce the uncertainty of the model about how to ask a question or what to copy, such as “White House” in question 2 of Fig. 1. In our model, we directly use our previously mentioned copy word labeling strategy to assign a binary label to each input word to indicate whether it is a clue word or not.

To predict the potential clue words in input, we have designed a novel clue prediction model that combines the syntactic dependency tree of an input with Graph Convolutional Networks. The intuition underlying our model design is that: the words copied from an input sentence to an output question are usually closely related to the answer chunk, and the patterns of the dependency paths between the copied words and answer chunks can be captured via Graph Convolutional Networks. Combining the graphical representation of input sentences with GCN enables us to incorporate both word features, such as word vector representation, Named Entity (NE) tags, Part-of-Speech (POS) tags and so on, and the syntactic structure of sentences for clue words prediction.

To generate questions given an answer chunk and the predicted clue words distribution in an input sentence, we apply the Seq2Seq framework which contains our feature-rich sentence encoder and a decoder with attention and copy mechanism. The clue word prediction results are incorporated into the encoder by a binary feature embedding. Based on our multi-task labeling strategy, i.e., labeling for both clue prediction and question generation, we jointly learn different components of our model in a supervised manner.

We performed extensive evaluation on two large question answering datasets: the SQuAD dataset v1.1 (Rajpurkar et al., 2016) and the NewsQA dataset (Trischler et al., 2016). For each dataset, we use the answer chunk and the sentence that contains the answer chunk as input, and try to predict the question as output. We compared our model with a variety of existing rule-based and neural network based models. Our approach achieves significant improvement by jointly learning the potential clue words distribution for copy and the encoder-decoder framework for generation, and out-performs state-of-the-art approaches.

Figure 2. An example to show the syntactic structure of an input sentence. Clue words “White House” and “today” are close to the answer chunk “Barack Obama” with respect to the graph distance, though they are not close to each other in terms of the word order distance.

2. Problem Definition and Motivation

In this section, we formally introduce the problem of question generation, and illustrate the motivation behind our work.

2.1. Answer-aware Question Generation

Let us denote a passage by , a question related to this passage by , and the answer of that question by . A passage can be either an input sentence or a paragraph, based on different datasets. A passage consists of a sequence of words where denotes the length of . A question contains words from either a predefined vocabulary or from the input text

. The task is finding the most probable question

given an input passage and an answer:

Figure 3. An example from the SQuAD dataset. Our task is to generate questions given an input passage and an answer. In SQuAD dataset, answers are sub spans of the passages.

Fig. 3 shows an example of the dataset used in our paper. Note that the answer of the question is limited to sub spans of the input passage. However, our work can be easily adapted to the cases where the answer is not a sub span of the input passage by adding an extra answer encoder.

2.2. What to Ask: Clue Word Prediction

Even given the answer of a desired question, the task of question generation is still not a one-to-one mapping, as there are potentially multiple aspects related to the given answer. For example, in Fig. 2, given the answer chunk “Barack Obama”, the questions “The speech in the White House is given by whom?”, “Who gives a speech on democracy?”, and “Today who gives a speech?” are all valid questions. As we can see, although these questions share the same answer, they can be asked in different ways based on which word or phrase we choose to copy (e.g., “White House”, “democracy”, or “today”).

To resolve this issue, we propose to predict the potential “clue words” in the input passage. A “clue word” is defined as a word or phrase that is helpful to guide the way we ask a question, such as “White House”, “democracy”, and “today” in Fig. 2

. We design a clue prediction module based on the syntactical dependency tree representation of passages and Graph Convolutional Networks. Graph Convolutional Networks generalize Convolutional Neural Networks to graph-structured data, and have been successfully applied to natural language processing tasks, including semantic role labeling

(Marcheggiani and Titov, 2017), document matching (Liu et al., 2018; Zhang et al., 2018a), relation extraction (Zhang et al., 2018b), and so on. The intuition underlying our model design is that clue words will be more closely connected to the answer in dependency trees, and the syntactical connection patterns can be learned via graph convolutional networks. For example, in Fig. 2, the graph path distances between answer “Barack Obama” to “democracy”, “White House”, and “today” are , , and , while the corresponding word order distances between them are , , and , respectively.

2.3. How to Ask: Copy or Generate

Another important problem of question generation is when to choose a word from the target vocabulary and when to copy a word from the input passage during the generation process. People tend to repeat or paraphrase some text pieces in a given passage when they ask a question about it. For instance, in Fig. 3, the question “Who along with Russia supported post WW-II communist movements”, the text pieces “supported”, “post”, and “communist movements” are repeating the input passage, while “Russia” and “WW-II” are synonymous replacements of input phrases “The Soviet Union” and “World War II”. Therefore, the copied words in questions should not be restricted to out-of-vocabulary words. Although this phenomenon is well-known by existing approaches, they do not properly and explicitly distinguish whether a word is from copy or from generate.

In our model, we consider the non-stop words that are shared by both an input passage and the output question as clue words, and encourage the model to copy clue words from input. After labeling clue words, we train a GCN-based clue predictor to learn whether each word in source text can be copied to the target question. The predicted clue words are further fed into a Seq2Seq model with attention and copy mechanism to help with question generation. Besides, different from existing approaches that share a vocabulary between source passages and target questions, we reduce the size of the target vocabulary of questions to be smaller than source passages and only include words with word frequency higher than a threshold. The idea is that the low-frequency words in questions are usually copied from source text rather than generated from a vocabulary. By combining the above strategies, our model is able to learn when to generate or copy a word, as well as which words to copy or generate during the progress of question generation. We will describe our operations in more detail in the next section.

3. Model Description

Figure 4. Illustration of the overall architecture of our proposed model. It contains a GCN-based clue word predictor, a masked feature-rich encoder for input passages, and an attention-and-copy-based decoder for generating questions.

In this section, we introduce our proposed framework in detail. Similar to (Zhou et al., 2017; Serban et al., 2016; Du et al., 2017), our question generator is based on an encoder-decoder framework, with attention and copying mechanisms incorporated. Fig. 4 illustrates the overall architecture and detailed components in our model. Our model consists of three components: the clue word predictor, passage encoder and question decoder.

The clue word predictor predicts potential clue words that may appear a target question, based on the specific context of the input passage (without knowing the target question). It utilizes a syntactic dependency tree to reveal the relationship of answer tokens relative to other tokens in a sentence. Based on the tree representation of the input passage, we predict the distribution of clue words by a GCN-based encoder applied on the tree and a Straight-Through (ST) Gumbel-Softmax estimator

(Jang et al., 2016) for clue word sampling. The outputs of the clue word predictor are fed to the passage encoder to advise the encoder about what words may potentially be copied to the target question.

The passage encoder incorporates both the predicted clue word distribution and a variety of other feature embeddings of the input words, such as lexical features, answer position indicators, etc. Combined with a proposed low-frequency masking strategy (a shortlist strategy to reduce complexity on tuning input word embeddings), our encoder learns to better capture the useful input information with fewer trainable parameters.

Finally, the decoder jointly learns the probabilities of generating a word from vocabulary and copying a word from the input passage. At the decoder side, we introduce a multitask learning strategy to intentionally encourage the copying behavior in question generation. This is achieved by explicitly labeling the copy gate with a binary variable when a (non-stop) word in the question also appears in the input passage in the training set. Other multi-task labels are also incorporated to accurately learn when and what to copy. We further shortlist the target vocabulary based on the frequency distribution of non-overlap words. These strategies, assisted by the encoded features, help our model to clearly learn which word in the passage is indeed a clue word to be copied into the target question, while generating other non-clue words accurately.

The entire model is trained end-to-end via multitask learning, i.e., by minimizing a weighted sum of losses associated with different labels. In the following, we first introduce the encoder and decoder, followed by a description of the clue word predictor.

3.1. The Passage Encoder with Masks

Our encoder is based on bidirectional Gated Recurrent Unit (BiGRU)

(Chung et al., 2014), taking word embeddings, answer position indicators, lexical and frequency features of words, as well as the output of the clue word predictor as the input. Specifically, for each word in input passage , we concatenate the following features to form a concatenated representation to be input into the encoder:

  • Word Vector. We initialize each word vector by Glove embedding (Pennington et al., 2014). If a word is not covered by Glove, we initialize its word vector randomly.

  • Lexical Features

    . We perform Named Entity Recognition (NER), Part-of-Speech (POS) tagging and Dependency Parsing (DEP) on input passages using spaCy

    (Matthew Honnibal, 2015), and concatenate the lexical feature embedding vectors.

  • Binary Features. We check whether each word is lowercase or not, whether it is a digit or like a number (such as word “three”), and embed these features by vectors.

  • Answer Position. Similar to (Zhou et al., 2017), we utilize the B/I/O tagging scheme to label the position of a given answer, where a word at the beginning of an answer is marked with B, I denotes the continuation of the answer, while words not contained in an answer are marked with O.

  • Word Frequency Feature. We derive the word vocabulary from passages and questions in the training dataset. We then rank all the words in a descending order in terms of word frequencies, such that the first word is the most frequent word. The top words are labeled as frequent words. Words ranked between and are labeled as intermediate words. The remaining with rank lower than are labeled as rare words, where and are two predefined thresholds. In this way, each word will be assigned a frequency tag L (low frequency), H (highly frequent) or M (medium frequency).

  • Clue Indicator Feature. In our model, the clue predictor (which we will introduce in more detail in Sec. 3.3) assigns a binary value to each word to indicate whether it is a potential clue word or not.

Denote an input passage by . The BiGRU encoder reads the input sequence and produces a sequence of hidden states to represent the passage . Each hidden state is a concatenation of a forward representation and a backward representation:

where and are the forward and backward hidden states of the -th token in , respectively.

Furthermore, rather than learning the full representation for every word in the vocabulary, we use a masking strategy to replace the word embeddings of low-frequency words with a special ¡l¿ token, such that the information of low-frequency words is only represented by its answer/clue indicators and other augmented feature embeddings, except the word vectors. This strategy can improve performance due to two reasons. First, the augmented tagging features of a low-frequency word tend to be more influential than the word meaning in question generation. For example, given a sentence “¡PERSON¿ likes playing football.”, a question that can be generated is “What does ¡PERSON¿ like to play?”—what the token “¡PERSON¿” exactly is does not matter. This way, the masking strategy helps the model to omit the fine details that are not necessary for question generation. Second, the number of parameters that need be learned, especially the number of word embeddings that need be tuned, is largely reduced. Therefore, masking out unnecessary word embeddings while only keeping the corresponding augmented features and indicators does not hurt the performance of question generation. It actually improves training by reducing the model complexity.

(a) Rank Distribution of All Question Words
(b) Rank Distribution of Generated Question Words
(c) Rank Distribution of Copied Question Words
Figure 5. Comparing the rank distributions of all question words, words from generation, and words from copy.

3.2. The Question Decoder with Aggressive Copying

In the decoding stage, we utilize another GRU with copying mechanism to generate question words sequentially based on the encoded input passage representation and previously decoded words. We first initialize the hidden state of the decoder GRU by passing the last backward encoder hidden state to a linear layer:

For each decoding time step , the GRU reads the embedding of the previous word , previous attentional context vector , and its previous hidden state to calculate its current hidden state:

The context vector for time step is generated through the concatenated attention mechanism (Luong et al., 2015). Attention mechanism calculates a semantic match between encoder hidden states and the decoder hidden state. The attention weights indicate how the model spreads out the amount it cares about different encoder hidden states during decoding. At time step , the attention weights and the context vector are calculated as:

Combining the previous word embedding , the current decoder state and the current context vector , we can calculate a readout state by an MLP maxout layer with dropouts (Goodfellow et al., 2013)

. Then the readout state is passed to a linear layer and a softmax layer to predict the probabilities of the next word over the decoder vocabulary:

where is a -D vector.

The above module generates question words from a given vocabulary. Another important method to generate words is copying from source text. Copy or point mechanism (Gulcehre et al., 2016) was introduced into sequence-to-sequence models to allow the copying of unknown words from the input text, which solves the out-of-vocabulary (OOV) problem. When decoding at time step , the probability of copying is given by:

is the Sigmoid function, and

is the probability of copying. For the copy probability of each input word, we reuse the attention weights given by Equation (8).

Copying mechanism has also been used in question generation (Zhou et al., 2017; Song et al., 2018; Hu et al., 2018), however, mainly to solve the OOV issue. Here we leverage the copying mechanism to enable the copying of potential clue words, instead of being limited to OOV words, from input. Different from existing methods, we take a more aggressive approach to train the copying mechanism via multitask learning based on different labels. That is, when preparing the training dataset, we explicitly label a word in a target question as a word copied from the source passage text if it satisfies all the following criteria:

i) it appears in both source text and target question;

ii) it is not a stop word;

iii) its frequency rank in the vocabulary is lower than a threshold .

The remaining words in the question are considered as being generated from the vocabulary. Such a binary label (copy or not copy), together with which input word the question word is copied from, as well as the target question, are fed into different parts of the decoder as labels for multi-task model training. This is to intentionally encourage the copying of potential clue words into the target question rather than generating them from a vocabulary.

The intuition behind such an aggressive copying mechanism can be understood by checking the frequency distributions of both generated words and copied words (as defined above) in the training dataset of SQuAD. Fig. 5 shows the frequency distributions of all question words, and then of generated question words and copied question words. The mean and median rank of generated words are 2389 and 1032, respectively. While for copied words, they are 3119 and 1442. Comparing Fig. 5(b) with Fig. 5(c), we can see that question words generated from the vocabulary tends to be clustered toward high ranked (or frequent) words. On the other hand, the fraction of low ranked (or infrequent) words in copied words are much greater than that in generated words. This means the generated words are mostly from frequent words, while the majority of low-frequency words in the long tail are copied from the input, rather than generated.

This phenomenon matches our intuition: when people ask a question about a given passage, the generated words tend to be commonly used words, while the repeated text chunks from source passage may contain relatively rare words such as names and dates. Based on this observation, we further propose to reduce the vocabulary at the decoder for target question generation to be top frequently generated words, where is a predefined threshold that varies according to datasets.

3.3. A GCN-Based Clue Word Predictor

Given a passage and some answer positions, our clue word predictor aims to identify the clue words in the passage that can help to ask a question and are also potential candidates for copying, by understanding the semantic context of the input passage. Again, in the training dataset, the non-stop words that are shared by both an input passage and an output question are aggressively labeled as clue words.

We note that clue words are, in fact, more closely connected to the answer chunk in the form of syntactic dependency trees than in word sequences. Fig. 6 shows our observation on the SQuAD dataset. For each training example, we get the nonstop words that appear in both the input passage and the output question. For each clue word in the training set, we find its shortest undirected path to the answer chunk based on the dependency parsing tree of the passage. For each jump on the shortest path, we record the dependency type. We also calculate the distance between each clue word and the answer in terms of the number of words between them. As shown in Fig. 6(a), prep, pobj, and nsubj appear frequently on these shortest paths. Comparing Fig. 6(b) with Fig. 6(c), we can see that the distances in terms of dependency trees are much smaller than those in terms of sequential word orders. The average and median distances on the dependency trees are and , while those values are and for sequential word distances.

In order to predict the positions of clue words based on their dependencies on the answer chunk (without knowing the question which is yet to be generated), we use a Graph Convolutional Network (GCN) to convolve over the word features on the dependency tree of each passage, as shown in Fig. 4. The predictor consists of four layers:

Embedding layer. This layer shares the same features with the passage encoder, except that it does not include the clue indicators. Therefore, each word is represented by its word embedding, lexical features, binary features, the word frequency feature, and an answer position indicator.

Syntactic dependency parsing layer. We obtain the syntactic dependency parsing tree of each passage by spaCy (Matthew Honnibal, 2015), where the dependency edges between words are directed. In our model, we use the syntactic structure to represent the structure of passage words.

Encoding layer. The objective of the encoding layer is to encode the context information into each word based on the dependency tree. We utilize a multi-layered GCN to incorporate the information of neighboring word features into each vertex, which is a word. After GCN layers, the hidden vector representation of each word will incorporate the information of its neighboring words that are no more than hops away in the dependency tree.

Output layer. After obtaining the context-aware representation of each word in the passage, we calculate the probability of each word being a clue word. A linear layer is utilized to calculate the unnormalized probabilities. We subsequently sample the binary clue indicator for each word through a Gumbel-Softmax layer, given the unnormalized probabilities. A sampled value of indicates that the word is predicated as a clue word.

(a) Distribution of Syntactic Dependency Types between Copied Words and the Answer
(b) Distribution of Syntactic Dependency Distances between Copied Words and theAnswer
(c) Distribution of Sequential Word Distances between Copied Words and the Answer
Figure 6. Comparing the distributions of syntactic dependency distances and sequential word distances between copied words and the answer in each training sample.

3.3.1. GCN Operations on Dependency Trees

We now introduce the operations performed in each GCN layer (Kipf and Welling, 2016; Zhang et al., 2018b). GCNs generalize the CNN from low-dimensional regular grids to high-dimensional irregular graph domains. In general, the input to a GCN is a graph with vertices , and edges . The edges can be weighted with weights , or unweighted. The input also contains a vertex feature matrix denoted by , where is the feature vector of vertex .

Since a dependency tree is also a graph, we can perform the graph convolution operations on dependency trees by representing each tree into its corresponding adjacency matrix form. Now let us briefly introduce the GCN propagation layers used in our model (Zhang et al., 2018b). The weighted adjacency matrix of the graph is denoted as where . For unweighted graphs, the weights are either 1 or 0. In an -layer GCN, let denotes the input vector and denotes the output vector of node at the -th layer. We will utilize a multi-layer GCN with the following layer-wise propagation rule (Zhang et al., 2018b):

is a nonlinear function (e.g., ReLU), and

is a linear transformation.

where is an identity matrix. is the degree of node (or word in our case) in the graph (or dependency tree).

In our experiments, we treat the dependency trees as undirected, i.e., . Besides, as we already included the dependency type information in the embedding vectors of each word, we do not need to incorporate the edge type information in the adjacency matrix.

Stacking this operation by layers gives us a deep GCN network. The input to the nodes in the first layer of the GCN are the feature vectors of words in the passage. After layers of transformations, we can obtain a context-aware representation of each word. We then feed them into a linear layer to get the unnormalized probability of each word being a clue word. After that, the unnormalized probabilities are fed to a Straight-Through (ST) Gumbel-Softmax estimator to sample an -dimensional binary vector indicating whether each of the words is a clue word or not.

3.3.2. Gumbel-Softmax

Gumbel-Softmax (Jang et al., 2016)

is a method of sampling discrete random variables in neural networks. It approximates one-hot vectors sampled from a categorical distribution by making them continuous, therefore the gradients of model parameters can be calculated using the reparameterization trick and the standard backpropagation. Gumbel-Softmax distribution is motivated by Gumbel-Max trick

(Maddison et al., 2014), an algorithm for sampling from a categorical distribution. Let denotes a -dimensional categorical distribution where the probability of class is defined as:

where is the unnormalized log probability of class . We can easily draw a one-hot sample from the distribution by the following equations:

where is Gumbel noise used to perturb each . In this way, taking is equivalent to drawing a sample using probabilities .

Gumbel-Softmax distribution replaces the function by differentiable softmax function. Therefore, a sample drawn from Gumbel-Softmax distribution is given by:

where is a temperature parameter. The Gumbel-Softmax distribution resembles the one-hot sample when diminishes to zero.

Straight-Through (ST) Gumbel-Softmax estimator (Jang et al., 2016) is a discrete version of the continuous Gumbel-Softmax estimator. It takes different paths in the forward and backward propagation. In the forward pass, it discretizes a continuous probability vector sampled from the Gumbel-Softmax distribution into a one-hot vector by:

In the backward pass, it uses the continuous , so that the error signal can still backpropagate.

Using the ST Gumbel-Softmax estimator, our model is able to sample a binary clue indicator vector for an input passage. Then the clue indicator vector is fed into the passage encoder for question generations, as shown in Fig. 4.

4. Evaluation

In this section, we evaluate the performance of our proposed models on the SQuAD dataset and the NewsQA dataset, and compare them with state-of-the-art question generation models.

4.1. Datasets, Metrics and Baselines

Dataset Train Dev Test P-Length Q-Length A-Length
  • P-Length: average number of tokens of passages.
    Q-Length: average number of tokens of questions.
    A-Length: average number of tokens of answers.

Table 1. Description of evaluation datasets.

The SQuAD dataset is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text from the corresponding reading passage. SQuAD 1.1 is used in our experiment containing 536 Wikipedia articles and more than 100K question-answer pairs. When processing a sample from dataset, instead of using the entire document, we take the sentence that contains the answer as the input. Since the test set is not publicly available, we use the data split proposed by (Zhou et al., 2017) where the original dev set is randomly split into a dev test and a test set of equal size.

In the NewsQA dataset, there are 120K questions and their corresponding answers as well as the documents that are CNN news articles. Questions are written by questioners in natural language with only the headlines and highlights of the articles available to them. With the information of the questions and the full articles, answerers select related sub-spans from the passages of the source text and mark them as answers. Multiple answers may be provided to a same question by different answerers and they are ranked by validators based on the quality of the answers. In our experiment, we picked a subset of NewsQA where answers are top-ranked and are composed of a contiguous sequence of words within the input sentence of the document.

Table 1 shows the number of samples in each set and the average number of tokens of the input sentences, questions, and answers listed in columns P-Length, Q-Length, and A-Length respectively.

We report the evaluation results with following metrics.

  • BLEU (Papineni et al., 2002). BLEU measures precision by how much the words in prediction sentences appear in reference sentences at the corpus level. BLEU-1, BLEU-2, BLEU-3, and BLEU-4, use 1-gram to 4-gram for calculation, respectively.

  • ROUGE-L (Lin, 2004). ROUGE-L measures recall by how much the words in reference sentences appear in prediction sentences using Longest Common Subsequence (LCS) based statistics.

  • METEOR (Denkowski and Lavie, 2014)

    . METEOR is based on the harmonic mean of unigram precision and recall, with recall weighted higher than precision.

In the experiments, we have eight baseline models for comparison. Results reported on PCFG-Trans, MPQG, and NQG++ are from experiments we conducted using published code on GitHub. For other baseline models, we directly copy the reported performance given in their papers. We report all the results on the SQuAD dataset, and for the NewsQA dataset, we can only report the baselines with open source code available.

  • PCFG-Trans (Heilman, 2011)

    is a rule-based system that generates a question based on a given answer word span.

  • MPQG (Song et al., 2018) proposed a Seq2Seq model that matches the answer with the passage before generating the question.

  • SeqCopyNet (Zhou et al., 2018) proposed a method to improve the copying mechanism in Seq2Seq models by copying not only a single word but a sequence of words from the input sentence.

  • seq2seq+z+c+GAN (Yao et al., 2018) proposed a model employed in GAN framework using the latent variable to capture the diversity and learning disentangled representation using the observed variable.

  • NQG++ (Zhou et al., 2017) proposed a Seq2Seq model with a feature-rich encoder to encode answer position, POS and NER tag information.

  • Answer-focused Position-aware model (Sun et al., 2018) incorporates the answer embedding to help generate an interrogative word matching the answer type. And it models the relative distance between the context words and the answer for the model to be aware of the position of the context words when generating a question.

  • s2sa-at-mp-gsa (Zhao et al., 2018) proposed a model which contains a gated attention encoder and a maxout pointer decoder to address the challenges of processing long text inputs. This model has a paragraph-level version and a sentence-level version. For the purpose of fair comparison, we report the results of the sentence-level model to match with our settings.

  • ASs2s (Kim et al., 2018) proposed an answer-separated Seq2Seq to identify which interrogative word should be used by replacing the target answer in the original passage with a special token.

For our models, we evaluate the following versions:

  • CGC-QG (no feature-rich embedding). We name our model as Clue Guided Copy for Question Generation (CGC-QG). In this variant, we only keep the embedding of words, answer position indicators, and clue indicators for each token, and remove the embedding vectors of other features.

  • CGC-QG (no target reduction). This model variant does not contain target vocabulary reduction operation.

  • CGC-QG (no clue prediction). The clue predictor and clue embedding are removed in model variant.

  • CGC-QG. This is the complete version of our proposed model.

4.2. Experiment Settings

We implement our models in PyTorch 0.4.1

(Paszke et al., 2017) and train the model with a single Tesla P40. We utilize spaCy (Matthew Honnibal, 2015) to perform dependency parsing and extract lexical features for tokens. As to the vocabulary, we collect it from the training dataset and keep the top 20K most frequent words for both datasets.

We set the threshold and . For target vocabulary reduction, we set . The embedding dimension of word vector is set to be and initialized by GloVe. The word vectors of words that are not contained in GloVe are initialized randomly. The word frequency features are embedded to 32-dimensional vectors, and other features and indicators are embedded to 16-dimensional vectors. All embedding vectors are trainable in the model. We use a single layer BiGRU with hidden size 512 for the encoder, and a single layer undirected GRU with hidden size 512 for the decoder. The dropout rate is applied to the encoder, decoder, and the attention module.

During training, we optimize the Cross-Entropy loss function for clue prediction, question generation, and question copying, and perform gradient descent by the Adam

(Kingma and Ba, 2014) optimizer with an initial learning rate , two momentum parameters are and respectively, and

. The mini-batch size for each update is set to 32 and model is trained for up to 10 epochs (as we found that usually the models derive the best performance after

epochs). We apply gradient clipping with range

for Adam. Besides, exponential moving average is applied on all trainable variables with a decay rate . When testing, beam search is conducted with beam width . The decoding process stops when a token ¡EOS¿ that represents end of sentence is generated.

4.3. Main Results

Table. 2 and Table. 3

compare the performance of our model with existing question generation models on SQuAD and NewsQA in terms of different evaluation metrics. For the SQuAD dataset, we compare our model with all the baseline methods we have listed. As to the NewsQA dataset, since only a part of the baseline methods made their code public, we compare our model with approaches that have open source code. We can see that our model achieves the best performance on both datasets and significantly outperforms state-of-the-art algorithms. On the SQuAD dataset, given an input sentence and an answer, the BLEU-4, ROUGE-L, and METEOR of our result are

, , and respectively, while corresponding previous state-of-the-art results are , , and from different approaches. Similarly, our method also gives a significantly better performance on NewsQA compared with the baselines in Table 3.

The reason is that we combine different strategies in our model to make it learn when to generate or copy a word, and what to generate or copy. First, our model learns to predict clue words through a GCN-based clue predictor. Second, our encoder incorporates a variety of embeddings of different features and clue indicators. Combining with the masking strategy, our model can better discover the relationship between input patterns and output patterns. Third, the reduced target vocabulary also helps our model to better capture when to copy or generate, and the generator is easier to train with a reduced vocabulary size. And most importantly, our new criteria of marking a question word as copied word (as described in Sec.3.3) helps the model to make better decisions on which path to go, i.e., to copy or to generate, during question generation. By incorporating part of these new strategies and modules into our model, we can achieve performance better than state-of-the-art models on SQuAD and NewsQA. With all these designs implemented, our model gives the best performance on both datasets.

There is a significant gap between the performance on SQuAD and on NewsQA due to the different characteristics of the datasets. The average answer length of NewsQA is 44.2% larger than it of SQuAD according to the statistics shown in Table 1. Long answers usually hold more information and are more difficult to generate questions. Furthermore, reference questions in NewQA tend to have less strict grammars and more diverse phrasings. To give a typical example, “Iran criticizes who?” is a reference question in NewsQA which does not start with an interrogative word but ends with one. These characteristics make the performance on NewsQA not as good as on SQuAD. However, our approach is still significantly better than the compared approaches on NewsQA dataset. It demonstrates that copy from the input is a general phenomenon across different datasets. Our model better captures what copied words are and what generated words are in a question based on our new criteria of labeling a question word as copied word or not.

Answer-focused Position-aware model
CGC-QG (no feature-rich embedding)
CGC-QG (no target reduction)
CGC-QG (no clue prediction)
  • Experiments are conducted on baselines followed by a “” using released source code. Results of other baselines are copied from their papers where unreported metrics are marked “”.

Table 2. Evaluation results of different models on SQuAD dataset.
CGC-QG (no feature-rich embedding)
CGC-QG (no target reduction)
CGC-QG (no clue prediction)

Table 3. Evaluation results of different models on NewsQA dataset.

4.4. Analysis

We evaluate the impact of different modules in our model by ablation tests. Table 2 and Table 3 list the performance of our model variants with different sub-components removed.

When we remove the extra feature embeddings in our model, i.e., the embeddings of POS, NER, Dependency Types, word frequency levels (low-frequent, median-frequent, high-frequent), and binary features (whether it is lowercase, digit), the performance drops significantly. This is because the tags and feature embeddings represent each token in different aspects. The number of different tags is much smaller than the number of different words. Therefore, the patterns which can be learned from these tags and features are more obvious than what we can learn from word embeddings. Even though a well-trained word vector may contain the information of other features such as POS or NER, explicitly concatenating these feature embedding vectors helps the model to capture the patterns to ask a question more easily.

Removing the operation of target vocabulary reduction also hurts the performance of our model. As we discussed earlier, the non-overlap question words (or generated words) are mostly covered by the high frequency words. Reducing the target vocabulary size helps our model to better learn the probabilities of generating these words. On the other hand, it also encourages the model to better capture what they can copy from input text.

Finally, without the clue prediction module, the performance also drops on both datasets. This is because when given an answer span in a passage, asking a question about it is still a one-to-many mapping problem. Our clue prediction module learns how people select the related clue words to further reduce the uncertainty of how to ask a question by learning from a large training dataset. With predicted clue indicators incorporated into the encoder of generator, our model can fit the way how people ask questions in the dataset.

5. Related Work

In this section, we review related works on question generation and graph convolutional networks.

Existing approaches for question generation can be mainly classified into two classes: heuristic rule-based approaches and neural network-based approaches. The rule-based approaches rely on well-designed rules or templates manually created by human to transform a piece of given text to questions

(Heilman and Smith, 2010; Heilman, 2011; Chali and Hasan, 2015). However, they require creating rules and templates by experts which is extremely expensive. Also, rules and templates have a lack of diversity and are hard to generalize to different domains.

Compared with rule-based approaches, neural network-based models are trained end-to-end and do not rely on hand-crafted rules or templates. Most of the neural question generation models consider the task as Seq2Seq and take advantage of the encoder-decoder framework with attention mechanism. (Serban et al., 2016) utilizes an encoder-decoder framework with attention mechanism to generate factoid questions from FreeBase. (Du et al., 2017) generates questions from SQuAD passages based on a sequence-to-sequence model with attention mechanism.

However, given an input sentence, generating questions is a one-to-many mapping, as we can ask different questions from different aspects. Purely relying on Seq2Seq model may not be able to learn such a one-to-many mapping (Gao et al., 2018). To resolve this issue, recent works assume the aspect is known when generating a question (Zhou et al., 2017; Yuan et al., 2017; Subramanian et al., 2017; Kim et al., 2018; Song et al., 2018) or can be detected by a third-party pipeline (Du and Cardie, 2018). (Zhou et al., 2017) enriches the sequence-to-sequence model with answer position indicator to indicate if the current word is an answer word or not, and further incorporates copy mechanism to copy words from the context when generating a question. (Song et al., 2018; Hu et al., 2018) fuse the answer information into input sentence first, and apply a Seq2Seq model with attention and copy mechanism to generate answer-aware questions. (Gao et al., 2018) takes question difficulty into account by a difficulty estimator, and generate questions on different difficulty levels. (Sun et al., 2018) enriches the model with both answer embedding and the relative distance between the context words and the answer. (Tang et al., 2017; Tang et al., 2018) model question answering and question generation as dual tasks. They found that jointly training the two tasks helped to generate better questions.

We argue that even with an answer indicator, the problem of question generation is still a one-to-many mapping. To solve this problem, we enrich the model with a “clue word” predictor, where a clue word means a word that is related to the aspect of the targeting output question and usually copied to the question. We represent an input sentence by its syntactic dependency tree and represent each word using feature-rich embeddings, and let the model learn to predict whether each word in context can be a clue word or not. Combining the clue prediction module with a Seq2Seq model with attention and copy mechanism, our model learns when to generate a word from a target vocabulary and when to copy a word from the input context.

Graph Convolutional Networks generalize Convolutional Neural Networks to graph-structured data, and have been developed and grown rapidly in scope and popularity in recent years (Kipf and Welling, 2016; Defferrard et al., 2016; Liu et al., 2018; Marcheggiani and Titov, 2017; Battaglia et al., 2018). Here we focus on the applications of GCNs on natural language. (Marcheggiani and Titov, 2017) applies GCNs over syntactic dependency trees as sentence encoders, and produces latent feature representations of words in a sentence for semantic role labeling. (Liu et al., 2018) matches long document pairs using graph structures, and classify the relationships of two documents by GCN. (Zhang et al., 2018b) proposes an extension of graph convolutional networks that is tailored for relation extraction. It pools information over dependency trees efficiently in parallel. In our paper, we apply GCN over the dependency tree of an input sentence, and predict the potential clue words in the sentence together with a Gumbel-Softmax estimator.

6. Conclusion

In this paper, we demonstrate the effectiveness of teaching the model to make decisions during the question generation process on which words to generate and to copy. We label the nonstop and overlap words between input passages and questions as copy targets and use such labels to train our model. Besides, we further observe that the distribution of generated question words are mostly common words with relative high frequency. Based on this observation, we reduce the vocabulary size for generating question words. To help the model better capture how to ask a question and alleviate the issue of one-to-many mapping when asking a question, we propose a GCN-based clue prediction module to predict which part of words can be a clue word to ask a question given an answer. It utilizes the syntactic dependency tree representation of a passage to encode the information of each token in the passage, and sample a clue indicator for each token using a Straight-Through (ST) Gumbel-Softmax estimator. Our simulation results on the SQuAD dataset and NewsQA dataset show that our model outperforms a range of existing state-of-the-art approaches significantly.


5 Tutorial: From Models to Interpreters

A core use case for algebraic is fast and cheap interpreter development. With the right tooling, throw-away interpreters can be an easy way to explore and validate language design choices quickly.

As it happens, Haskell is already pretty good at this. Algebraic data types and functional destructuring syntax make interpreters easy to read and write, and its type system keeps track of pervasive breaking changes for you. algebraic addresses the untyped half of this equation—algebraic data structures (ADTs sans typing constraints) with destructuring syntax.

In this series, we will implement three interpreters based on the models originally used to design algebraic itself:

  1. A core model based on the untyped λ-calculus,

  2. An extended syntax that compiles down to core constructs, and

  3. A hosted variant that borrows additional constructs from the implementing platform.

The tutorials are written in an informal style with code and discussion mixed together. If you prefer source code, it can be read all in one place on github.

    5.1 The Core Calculus

    5.2 A Syntax Extension

    5.3 Exposing the Host


Are Aliens Responsible For Mystery Monoliths Appearing Across The World?

Mahima Negi
·6-min read

Last month saw the occurrence of similar mysterious structures at random places like a desert on one side of the earth and then a hilltop located on the other side of the planet.

The structure is being identified as a Monolith— described as an obelisk, column, or a large statue composed of a single block of stone or a rock.

The first Monolith appeared in a desert, in the middle of nowhere, in Utah. It was discovered on November 18th, by state helicopter crews who were aiding wildlife biologists to count the bighorn sheep.

Soon after its discovery, the monolith invited a lot of curious travelers towards it, even though its exact location was not revealed.

This mysterious monolith was a three-sided reflective structure, made of stainless steel, and was roughly around eleven feet tall. It caused a great buzz on the internet. Its unnatural presence generated a lot of attention from people, journalists, and experts all around the world.

What happened next was even weirder, two weeks after its discovery it disappeared—leaving the world in awe.

Monolith Makes A Comeback

The story didn’t end there, on 26th November, shortly after the disappearance of Utah’s monolith, another similar structure was found in Romania!

Shocked by the series of events, people soon started speculating. Questions like “Is it the same monolith?”, “How did it travel so quickly?” were prompted.

This new monolith appeared on top of a small hill near an ancient Dacian fortress, its structure was hastier than the one in Utah, indicating that it was a different structure altogether.

After making a brief appearance, the strange piece of metal disappeared again, similarly to the one in Utah, adding even more confusion to the mystery.

…And The Mystery Continues

Days after the disappearance of monoliths from Utah and Romania, the presence of a new and third monolith was confirmed at Pine Mountains in Atascadero, California.

“The three-sided obelisk appeared to be made of stainless steel, 10-feet tall and 18 inches wide. The object was welded together at each corner, with rivets attaching the side panels to a likely steel frame inside,” reported the Atascadero News

This monolith too is similar to the ones that were found in Utah and Romania—a three-sided reflective structure made of stainless steel but this one is not fixed to the ground and can be easily pushed and moved, unlike the others.

A local hiker, Ray Johnson confirmed that the monolith was not there on Tuesday.

Also Read: Here’s Why Invisible Aliens May Exist Amongst Us And We’ll Never Know

Pranksters? Artists? Or Aliens?

The appearance and disappearance of monoliths in three places within the span of 15 days have invited a lot of speculation.

Naturally, the internet is full of conspiracy theories, memes, and trolls.

Various possible explanations and theories like ‘ a work of art’, ‘alien invasion’, and ‘silly pranks’ were making rounds on social media.

The local mayor of the village in Romania where the monolith was found was also compelled to issue a statement urging “those who think there is still life in the universe” not to panic.

People on the internet are convinced that aliens are behind this act, interesting theories like “aliens are trying to find the signal on earth” are being spread.

The object possesses a stark resemblance to the famous monolith from Stanley Kubrick’s 1968 film “2001: Space Odyssey”, signaling to some sci-fi fanatics that it might be an extraterrestrial device that affects humans.

Some people are dead serious and scared while others are hoping on this extraterrestrial train just for a good laugh.

A gallery owner suggested to the media that the appearance of the structure may be a tribute to the late artist John McCracken, who created similar-looking objects before he died in 2011, therefore it is also being theorized as a work of an artist.

Another possibility of it being an artwork comes from the history of the Land Art Movement that was active in the 1960s-70s, the movement inspired art made from earth and rejected commodification of the same.

New proof indicated human activity responsible for the disappearance of Utah’s monolith.

New data that surfaced on the internet this week show four men caught in a video, trying to remove the monolith found in Utah, one of them can be heard saying “leave no trace”, as reported in an article by The Guardian. Therefore, clearly, its removal had nothing to do with aliens.

It was photographer Ross Bernards who posted on his Instagram on Monday of ‘the object’s final moments’ that proved that there was nothing extraterrestrial about the technology as it appeared to be ‘sheets of metal riveted to a hollow wooden scaffold’.

A report gives details.

“Bernards drove six hours on Friday with three friends to take some pictures of the object by moonlight. After getting some shots, he said he heard some voices coming up the canyon, and four men appeared. Bernards wrote that he stepped away so the new group could also enjoy some time alone with the object, only to watch as they began shoving it.

“They gave a couple of pushes on the monolith and one of them said, ‘You better have got your pictures,'” Bernards wrote. “He then gave it a big push, and it went over, leaning to one side. He yelled back to his other friends that they didn’t need the tools. The other guy with him at the monolith then said ‘this is why you don’t leave trash in the desert”.”

The different structures and details of monoliths also indicate that they were human-made and replicated.

Realists believe that it’s most likely that people got bored with quarantine and tried to make 2020 more surreal than it already is.

It can also be a great prank to make people believe in alien activities but in any case, mischievous people were quick to follow and copy the original structure found in Utah and continue the prank by placing it in Romania.

A new report from Germany informed the internet that a famous phallic-like wooden structure that has been a tourist attraction for 4-5 years now in south-west Munich, has gone missing or apparently ‘been chopped down’, which can be a prank inspired by the series of monolith disappearances.

Where Next?

The mystery of Monolith remains unsolved and the internet is still buzzing about its latest appearance.

Prank or not, the question that has left everybody waiting in excitement is “when and where will it appear next?”

Image Credits: Google Images

Sources: Indian Express, The Guardian, India Today

Find The Blogger: @MNtweeting

This post is tagged under: Mystery, mysterious, monoliths, mysterious monolith, conspiracy theories, monolith theories, alien activity, alien invasion, alien technology, prank, hoax, work of art, art, artists, photographer, Utah, desert, Romania, California, Internet, Germany, Phallic structure, unsolved

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Significance Of The Lagna And The Lagna Lord In Vedic Astrology

The Lagna And The Lagna Lord

The rising sign or Lagna is the degree of the Rashi and Nakshatra that is rising on the eastern horizon at the time of the native’s birth. The significance of Lagna is the focal point of the Vedic horoscope. We calculate the houses from the Lagna Rashi. The house that the Lagna occupies is the first house. We calculate the Lagna is based on the date and time of birth. The place of birth is vital as a change of longitude will change the Lagna.

The Lagna does a full cycle of all the 12 Rashis in a day which means that the Lagna is in each Rashi for only two hours. While the Moon sign or Chandra Rashi tells us about the mind of a native the Lagna Rashi and Nakshatra tell us about the ideals, intelligence, appearance and the person himself/herself. It shows how other people will view the native. Lagna also tells us about the vitality, character, longevity, power and dignity of the person. It also signifies the head and face areas and tells us about this area of the person’s body.

Lagna Chart North Indian Style
Lagna Chart North Indian Style

Strength Of The Lagna Lord

The Lord of the Rashi in which the Lagna is placed is called the Lagna lord or the Lagnesha. The strength and house position of the Lagna Lord in Vedic astrology is one of the key points in a person’s horoscope. The placement of the Lagnesha tells where the person’s focus will be.

The Lagna in the very early or very late degrees of the Rashi makes it weak. Some astrologers consider this to be at lower than 3 degrees and 20 minutes or above 26 degrees and 40 minutes. When the Lagna is weak the mental and physical strength of the person suffers. This can be mitigated if there are other factors that strengthen the Lagna. The aspect of benefics and malefics upon the significance of Lagna influence it greatly.

The text Sarvartha Chintamani states that if a house contains or is aspected by its own lord and benefics while no malefic aspect it gets strengthened. If its lord is placed in the 5th, 9th, 2nd, 4th, 7th or 10th house without the influence of malefic planets, it gets strengthened and yields good results of that house. The text also states that if the Lagnesha is very strong, without the aspect of any malefic on it and is placed in a Kendra with the aspect of benefic planets, it confers long life, virtues and wealth to the person. The Lagnesha casting an aspect on the Lagna is always desirable regardless of whether the Lagnesha is benefic or malefic.

The book also states that if the Lagnesha is not eclipsed the person becomes famous. If the Lagnesha is well placed the person is prosperous and happy. On the other hand, if the Lagnesha is placed in a Dusthana (6,8 or 12) house, in the house of a malefic, in debilitation or in an enemy sign the person will lead an unhappy miserable life and in a despicable place amidst outcast or vile people. If the Lagnesha lacks strength he will suffer constantly facing many calamities and remain sad and sick.

The significance of Lagna and the Lagnesha tell us how that person will fare in the world and how they will be viewed and treated by people in general. A strong Lagna and Lagnesha indicate a healthy, wealthy and successful life.

Lagna Chart South Indian Style
Lagna Chart South Indian Style

Aspects On Lagna

When the planet that is aspecting the Lagna is benefic to the significance of Lagna, the results are good and when it is malefic the opposite is true.  If there is more than one planet aspecting the result will be a combination judged by the astrologer.

Sun: When the Sun aspects the Lagna the person will be valorous and hot-tempered. He or she will gain wealth from their parents and serve the King (government in modern times).

Moon: When the Moon aspects the Lagna the person will be fortunate, courteous, sympathetic, wealthy.

Mars: The aspect of Mars makes the person hot-tempered, adventurous, injury-prone, righteous and earn from bravery.

Mercury: Mercury’s aspect on the Lagna makes the person learned, intelligent and helps him/her earn by their intelligence. The person is famous and honourable.

Jupiter: When Jupiter aspects the Lagna it makes the person learned, religious, famous, helps him/her keep the company of virtuous people, and honoured by the King (government).

Venus: The aspect of Venus makes the person look youthful, good looking, wealthy and favoured by women.

Saturn: When Saturn aspects the Lagna the native is troubled by heavy odds and sickness. The person works hard but earns less. The person is dirty, unhappy, foolish and attached to elderly women.

Significations Of Lagna Lord Or Lagnesha Placement

Lagna Lord In The First House

The significance of Lagna in the first house or the Lagna itself is that the native will have high energy, vitality, good health and strong ideals. The Lagnesha in Lagna which is the house of intelligence makes them very intelligent. They hold firm to their principles and ideals. Lagna also represents the body of the person and hence the Lagnesha in Lagna Vedic astrology shows that the person will invest time, effort and money in beautifying and keeping their body fit.

Strength Vs Weakness
Strength Vs Weakness

Lagna Lord In The Second House

The  Lagnesha in the second house makes the person gainful scholarly, good-natured, happy, religious and honourable. Since the Lagnesha which the intelligence and ideals of the person are focussed on the second house which is the house of family, food and wealth the person will have a lot of interest and focus on these aspects of life. The second house is also the house of speech and resident of the Goddess Saraswati thus making the person scholarly and wise. The commitment to the family can also be extended to the whole community making the person honoured in society.

Lagna Lord In The Third House

When the Lagnesh is in the third house the person is very courageous, has all kinds of material luxury, is honourable, intelligent and happy. The third house is the house of courage, younger siblings, sexual morals, valour and communication. So naturally, the Lagnesh being here would make the person very courageous. In practical application, this makes the person a risk-taker and natural leader.

Lagna Lord In The Fourth House

When the Lagnesh is in the fourth house there will be a great attachment to the family especially the mother and interest in wealth, vehicles, property, education and spirituality. This makes a person receive a good education and be popular in society. This placement also forms a Raj Yoga and brings fulfilment of material desires.

Lagna Lord In The Fifth House

The fifth house is one of the trines that is related to religion and it is very auspicious to have the Lagnesh in the fifth house. Significance of Lagna in fifth house shows an inclination toward religious matters. The person will be intelligent, famous and liked by the government. This position affects the health of the native in childhood but strengthens the father’s position in life. Since the fifth house is related to education this position shows that the person will have educational accomplishments.

Lagna Lord In The Sixth House

The sixth house is the house of disease, enemies and competition. This position indicates childhood illnesses. If the Lagnesh is strong in this house it indicates a career related to disease and enemies which would be healthcare or the army. There is success in competitive activities such as sports and competitive exams.

Lagna Lord In The Seventh House

The placement of the Lagnesh in the seventh house makes the person an extrovert, god at business and very interested in relationships/partnerships especially marriage related matters.

Significance Of Lagna On Man's Life
Significance Of Lagna On Man’s Life

Lagna Lord In The Eighth House

The Lagnesh in the eighth house indicates a person who is a psychic, mystic, a good advisor and very interested in the occult. The person is miserly and tends to get involved in unethical relationships.

Lagna Lord In The Ninth House

The ninth house is a trine house and the Lagnesh in this house is a good placement. It makes the person a good orator, interested in travel, religious, fortunate, wealthy, with higher education and having good children. The person would also receive good fortune from their father.

Lagna Lord In The Tenth House

The Lagnesh in the tenth house makes a person very career-oriented resulting in professional success. The person is greatly influenced by the father.

Lagna Lord In The Eleventh House

The Lagnesh in the eleventh house makes a person-oriented towards profit-making and gain and also gives good results of the same. The person would have a large network circle of friends and also be very inclined towards their elder siblings.

Lagna Lord In The Twelfth House

The twelfth house placement of the Lagnesh gives an unhappy and lonely childhood probably far away from the place of birth. They will have much wasteful expenditure and be interested in lonely places, hospitals etc. The person would also show interest in matters related to travel, sex and lonely places.


what is a project programme

Monitoring is checking progress against plans. A program manager manages multiple projects, and sometimes multiple programs while a project manager manages the teams responsible for fulfilling the project and achieving its deliverables.. Generally speaking, a program manager has broader â¦ Project management is the planning, organizing and managing the effort to accomplish a successful project. Professionals who carry this misconception into the PMP® certification exam are the least likely ones to pass the exam. Monitoring allows results, processes and experiences to be documented and used as a basis to steer decision-making and learning processes. All deliver complex programmes and â¦ Of course, many of the office buildings that are built are similar in many respects but each individual facility is unique in its own way. According to the PMBOK (Project Management Body of Knowledge) 3rd edition, A project is defined as a temporary endeavor with a beginning and an end and it must be used to create a unique product, service or result. This definition lets make a distinction between other pieces of work, such as: 1. One view of project planning divides the activity into: A Project management plan is a formal, approved document that defines how the project is executed, monitored, and controlled. Program Framework is a Microsoft Gold Partner delivering Project, Programme and Portfolio Management solutions with Microsoft Cloud. Project for the web, formerly known as Project service, is Microsoft's most recent offering for cloud-based work and project management. Later, when you perform the Plan Scope, Estimate Costs, and Develop Schedule processes, you can revise the components of the project plan with more detail to reflect a deeper understanding of the project. Stream to Twitch, YouTube and many other providers or record your own videos with high quality H264 / AAC encoding. Privacy Policy, Similar Articles Under - Project Management, Bidding as an Essential Skill for Project Managers, Time/Material Billing & Fixed Cost Billing, Why Project Managers Should Not Overpromise and Underestimate at the Same Time, The Need for Person Independence in Software Project Management, The Importance of Ethics in Project Management, Why Most Projects Run into Problems and How to Avoid them and What to do about it, Why Project Managers Must Master the Art of Dealing with Disruptions, Why Project Managers Must Focus on Last Mile Connectivity to Ensure Success, How Project Managers Can Benefit from Knowledge Management Systems, Why Project Managers Must be Prepared to Deal with Disruptions of All Kinds, Transitioning to Agile? Any change to the scope of the project must have a matching change in budget, time, resources, or all three. Because major defense programs entail working with contractors, it is often called acquisition â¦ Take Simplilearn’s PMP Certification course to get all the training you need. As you will see in this article, a Project Management Plan is a document that defines how a project is executed, monitored, and controlled; it is much more than a schedule chart. A project proposal is a document that provides all the information required for project stakeholders to decide to initiate a project. This is in contrast to a program, (referred to a 'programme' in the UK) which is 1) an ongoing process, such as a quality control program, or 2) an activity to manage a number of multiple projects together. A project is a one-time activity that produces a specific output and or outcome, for example, a building or a major new computer system. Increasing his professional Seniority: Programme Development Officer, manages more development projects at once. This position requires great responsibility and proper time management because the job entails constant monitoring and control of all project variables. A program will rely more on a rolling plan, as multi-year programs will need to adjust their plans as the business changes. Entry requirements. Program management or programme management is the process of managing several related projects, often with the intention of improving an organization's performance. The project scope is the definition of what the project is supposed to accomplish and the budgets of time and money that have been created to achieve these objectives. Management Study Guide is a complete tutorial for management students, where students can learn the basics as well as advanced concepts related to management and its related subjects. Besides proper planning, a project manager’s abilities also lie in efficiently controlling the project and ensuring project deliverables are on time—and that the project is completed per the project management plan. The scope baseline, schedule baseline, and the cost baseline are collectively referred to as the performance measurement baseline. For the purpose of definition, the phases may be divided into initial charter, scope statement, plan, baseline, progress, acceptance, approval and handover. Subscribe to our YouTube Channel & Be a Part of 400k+ Happy Learners Community. For instance, if your project is less than three months old and has fewer than 20 people working on it, you may not be working in what is called a project according to the definition of the term. Program â a broad, long-term objective that is often decomposed into a series of projects and sub-projects 2. Project management includes developing a project plan, which invâ¦ The Essential Certificate is designed for people who are new to project and programme support office roles. The Project-Based Section 8 Rental Assistance program provides affordable apartment communities that are owned by private landlords with a rental subsidy that helps pay the rent for low income tenants. Each milestone at each stage is then elaborated and tracked for completion. This classification is according to the PMBOK. What this definition of a project means is that projects are those activities that cannot go on indefinitely and must have a defined purpose. A project administrator is a professional who organizes the necessary team members and specializes in facilitating, reporting and analyzing projects under the supervision of a project manager. Related: How Program Managers Use Software to Manage Project Portfolios The Difference Between a Program Manager and a Project Manager. The Project management plan is not created all at once. It is progressively elaborated, which means it is developed, refined, revisited, and updated. A project is an activity to meet the creation of a unique product or service and thus activities that are undertaken to accomplish routine activities cannot be considered projects. A project management plan is a collection of baselines and subsidiary plans that include: As per the PMBOK® Guide, the project management plan is an output of the Develop Project Management Plan process in the Project Integration Management Knowledge Area. Who grants the approval for the project management plan depends on the organizational structure and a number of other factors. It is essentially a rigorously defined project pitch â¦ As the work proceeds, the performance of the project is measured against the performance measurement baseline included in the project management plan. The project may be finite but not the result. For instance, a project to build a monument would be of fixed duration whereas the result that is the monument may be for an indefinite period in time. Project for the web provides simple, powerful work management capabilities and can be used by project managers and team members to plan and manage work of â¦ Finally, a project must be progressively elaborated. Most of the components of the project management plan are created in various processes defined in the PMBOK® Guide; for instance, the Communications Management Plan is developed in the Plan communications process. The project manager handles conflicting priorities in advance. The P3O ® guidance - which is aligned to PRINCE2 ®, MSP ®, and M_o_R ® - brings together a set of principles, processes and techniques to facilitate effective portfolio, programme and project management through enablement, challenge and support structures.. © Management Study Guide Typically the project plan is approved by the project manager, project sponsor, or the functional managers who provide the resources for the project. Documentation includes all actions required to define, prepare, integrate and coordinate additional plans. The phases of a project make up the project life cycle. Further, it is progressively elaborated. Project managers spend a substantial amount of time ensuring baselines are achieved, ensuring the project sponsor and the organization get the full benefits of their projects. In practice and in its aims, program management is often closely related to systems engineering, industrial engineering, change management, and business transformation. This means that a project is first defined initially and then as the project progresses, the definition is revisited and more clarity is added to the scope of the project as well as the underlying assumptions about the project. The customer signs the contract but often leaves the internal workings of the organization delivering the project. Typically the project plan is approved by the project manager, project sponsor, or the functional managers who provide the resources for the project. A pilot program, also called a feasibility study or experimental trial, is a small-scale, short-term experiment that helps an organization learn how a large-scale project might work in practice. Project is used in a variety of industries including construction, manufacturing, pharmaceuticals, government, retail, financial services and health care. Task â an identifiable and measurable activity that create a small unit of work for a related project 3. The Project manager creates the project management plan following inputs from the project team and the key stakeholders. Work package â division of a project task 4. It becomes less difficult for a project manager to get the project management plan approved, if: All the stakeholders are identified along with and their requirements and objectives. Thus, the naming of the phases of a project depends on the kind of deliverables that is sought at each phase. On the other hand, programs are definitely longer. Since you already have these things at the time you begin developing the project plan, you can go ahead and develop the scope management plan, cost management plan, and the schedule management plan. The build phase in a project delivers the completed code whereas the test phase is about the completed testing for the deliverables. A solid understanding of the project plan can pay rich dividends throughout your preparation for the PMP® certification exam, and also help in managing projects. A project is an activity to create something unique. An Introduction to Project Management: A Beginner’s Guide, Difference between Project Management Plan and Project Documents, What is Project Planning: Tools and Fundamentals, Project Management Tools for Effective Project Planning, preparation for the PMP® certification exam, Big Data Hadoop Certification Training Course, AWS Solutions Architect Certification Training Course, Certified ScrumMaster (CSM) Certification Training, ITIL 4 Foundation Certification Training Course, Data Analytics Certification Training Course, Cloud Architect Certification Training Course, DevOps Engineer Certification Training Course, Management plans for scope, schedule, cost, quality, human resources, communications, risk, and procurement. For instance, in a software project, the requirement phase needs to generate the requirement documents, the design phase the design document etc. However, the Schedule Management Plan, Cost Management Plan, and Scope Management Plan are created in the Develop Project Management Plan process. A project is an activity to meet the creation of a unique product or service and thus activities that are undertaken to accomplish routine activities cannot be considered projects. Every effort has a planning phase. Suitable for people with a basic working knowledge of IT. In organizations, a project is defined as a piece of work that is planned for implementation within current business environment. Each phase of the project is associated with a certain milestone and the set of deliverables that each phase is expected to deliver is then tracked for compliance and closure. Candidates should have basic working knowledge of IT. The program (or project) evaluation and review technique (PERT) is a statistical tool used in project management, which was designed to analyze and represent the tasks involved in completing a given project.. First developed by the United States Navy in 1958, it is commonly used in conjunction with the critical path method (CPM) that was introduced in 1957. What Project Managers Must Know About This Hot Methodology, Overcoming the Challenges of Delivering Projects on Time and Without Cost Overruns. A project management office (PMO) is a group â internal or external to a company â that sets, maintains and ensures standards for project management across that organization. In the defense sector, it is the dominant approach to managing very large projects. Are you ready to learn how to best handle project schedules, costs, the scope of work, project constraints, market competition, stakeholders, and more? Project vs Program Management Planning. This means that the project progresses in steps and continues by increments. PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc. It is convenient for the project managers to divide the project into phases for control and tracking purposes. If these adjustments fail to correct the deviations, then formal change requests to the baselines become necessary. We are a ISO 9001:2015 Certified Education Provider. Since the project management plan is a formal document that is used to manage the execution of the project, it must receive formal approval. It has always been practiced informally, but began to emerge as a distinct profession in the mid-20th century. Thus, the phases of a project are closely correlated with that of the project cycle. It becomes less difficult for a project manager to get the project management plan approved, if: To understand how to build a career in project management, and how certification training prepares you for project management job roles in any industry, watch this video on PMP Certification Training. Each of these processes is necessary to ensure that the project stays on track and is completed according to the specifications. For instance, a software project may have requirement, design, build, test, implementation phases whereas a project to build a metro or a building may have different names for each phase. *Lifetime access to high-quality, self-paced e-learning content. This also means that the definition of the project is refined at each step and ultimately the purpose of the progress is enunciated. Project planning is a procedural step in project management, where required documentation is created to ensure successful project completion. This document is used to define the approach project team takes to deliver the intended project management scope of the project. "Our customers come from all sectors and industries and range from small specialist SMEs to global brands. It has to be remembered that the term temporary does not apply to the result or service that is generated by the project. PMBOK®, PMP® and PMI® are registered trademarks of the Project Management Institute, Inc. Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

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Silver Bullion

Silver is a truly unique and timeless precious metal. It is demanded for both industrial purposes and as a secure store of value lasting throughout ancient civilization through modern times. There are a growing number of industrial uses for silver: smart phones, flat screen TVs and medical applications. Silver is a valuable and practical industrial commodity, as well as an appealing investment which has been used since Biblical Times.

Silver Numismatic

United States Numismatic Silver are legal tender coins manufactured before 1933 and are not eligible for IRA Investments. They are generally way over priced particularly when graded.


Prophet Patrick Buwemba
Card image

Double mindedness

Dec. 4, 2019

The Chariot Eye
Sunday, 22-09-19

Prophet Patrick Buwemba

Double mindedness

If you see any stagnation in life, this might be one of the causes of that delay in man's life. Stagnation is mostly caused by the character of a person who tends to give attention to many ideas instead of focusing on one idea at a time.
You can only move forward if you teach yourself to make decisions once and for all.

You are finding it hard to make decisions because you have not prioritized the aspects in your life. All aspects cannot be equally important in a person's life but they are considered just as important by you.
So for the sake of speed,you make the decision on what to give focus. When you come to God, let all your trust be in him.

Many have prayed and their life is not changed and they don't seem to understand what their problem is. Their problem is not their prayer, but in them doubting and this is caused by having different sources of faith.
If you will believe on God and have results, then your faith will stop being on these temporary things like jobs,money, human beings.
Our God is a jealous God so decide today who you will serve!!

Lord,my God, I put my trust in you .


Smarthinking Writer's Handbook

Evaluating Sources

Chapter 2: Section 3, Lesson 7

Academic papers and writing projects require the most relevant and authoritative sources you can find, but how do you know what the best sources are? Similarly, academic sources like journal articles and academic monographs (works on narrow, specialized topics) provide valuable, detailed information, but how can you tell whether the source you've found is a reliable one?

The Internet has changed how people perceive the credibility of sources; you need to be more aware than ever of the quality of your research. You’ll find a lot of resources in an online search: there are many highly credible academic blogs, but you’ll also find many journals that are not peer-reviewed; you’ll find biased and personal websites as well as websites that are professionally researched, edited, and published. The boundaries between research methods—like using the library, doing field research, and using the Internet—have broken down. Using the library now often means using the Internet to access the library, and conducting an interview often means using email to send the questions and gather answers. In order to effectively evaluate sources, you should first understand two important types of sources.

Types of Sources
Whether you find your source in print, online, or in a different medium, it will fall into one of two basic types—primary or secondary.

Primary Sources
A primary source is a document, object, communication, or other material from the time period or issue you’re studying. If you conduct field research, the information you gather is from a primary source. Any results of observations, surveys, or interviews are primary data, whether those surveys and interviews took place in person, by email, or by other electronic means. If you conduct an experiment in a biology or psychology class, the resulting data are primary. If you conduct a literary study, then the primary source is the piece of literature that you need to research. Primary sources include historical documents as well as physical objects such as artifacts.

Secondary Sources
In contrast, secondary sources are ones that comment on, analyze, critique, or reflect on primary sources. Secondary sources often include books about your subject, journal articles, blog entries, websites, newspaper articles, and social media commentary about a topic you’re researching. A secondary source may come from any number of publications discussing your topic.

Distinguishing Types
Both primary sources and secondary sources can be relevant to and necessary for a given research topic. Depending on your assignment, the topic you’re researching, and the requirements of your instructor, you might use only primary sources, only secondary sources, or a mix of both.

It can be easy to confuse the two types of sources, especially given the way the Internet has challenged the traditional boundaries mentioned above. A source that is “secondary” for one assignment can be “primary” for another assignment.

For example, if you were researching the Iraq War, first-hand reports from Iraq by citizens and soldiers would be primary sources, while articles, blog posts, and social media posts about the war from commentators, writers, and others would likely be secondary sources. However, if you were researching social media responses to the Iraq War, the social media posts themselves would be primary sources; newspaper articles about social media would be secondary sources.

If you’re confused as to whether your source is primary or secondary, try to identify its relationship to your topic. Is it a source that originates from the time period or issue you’re researching (that is, a primary source), or is it a derivative source that comments on, critiques, or analyzes the primary source (that is, a secondary source)?

Evaluating Sources
When conducting research, you should choose the best sources to use in the paper. The following list provides just a few of the criteria you can use to help you evaluate and choose sources:

Think about whether you actually need the information. Don’t use a source simply to lengthen your bibliography or essay. Every source should be both important to and necessary for your paper.

How is the author of the source identified? What connection does he or she have to the material? For example, does the author have a degree in the field in which he or she is writing? How extensively has he or she published in this area? If the source is not academic, what personal or professional connections does the author have to the topic? Look for the author’s biography or credentials in the source itself, or search for the author’s name online.

Credibility deals with whether the source is believable or trustworthy. What stated or unstated political, corporate, social, or ethical goals does the author carry? Separate from the author, who is hosting, publishing, or promoting the source and why? How might these factors, motivations, and goals affect the source’s credibility? How trustworthy is the author? Do there appear to be obvious or ambiguous omissions or even errors?

Publication Date
Books, journal articles, magazine editorials, field research, and websites often indicate a publication date. In electronic sources, the publication date can be confused with other dates, such as dates regarding the last time a website was docs. A blog will often display many dates—dates for individual blog entries, comments, replies, etc. Use the date that is most relevant to the item you’re considering. If a book lists multiple dates on its copyright page, the most recent date is usually the publication date for that edition. On a blog post, look for the date associated with the specific entry you’re reading. Note that undated sources aren’t necessarily worthless. Limited edition publications, primary sources, and historical material might be undated but still very valuable based on other criteria, such as author or relevance, especially if the date or time period can be guessed. Think twice about using undated sources with other warning signs, though. Such sources might include undated personal websites containing general information; anonymous, undated blog comments; and undated social media posts that can’t be traced to a specific user or person.

Evaluating the audience is a matter of considering for whom the source, information, or item was published, photographed, written, or compiled. Who did the author, creator or publisher hope would read or access this material? What is the author’s agenda or bias? For example, is the author hoping to influence a specific demographic, person, organization or group of people? Or is the author explicitly or implicitly advocating for a specific change of some kind, whether social, economic, or political? The same questions can be asked of the publisher, not just the author.

Length is an important factor for any source, but it’s not a defining one. An academic journal article can provide detailed information about a topic. An academic or professional blog can provide a concise opinion from a scholar in the field in less than a few hundred words. The amount and type of information your assignments require will help you determine which sources to use.

Think About It

  • What kind of research will you use—secondary sources, or primary sources, or both?
  • Where will you find these sources?
  • How will you know whether your sources are credible?

Evaluating your sources requires that you understand the type of source and that you think about its credibility based on at least some of the criteria discussed above.


BSR HERproject Impact Portal

A portal to record surveys and generate reports for their partners.

The HERproject seeks to empower women in the workforce around the world. BSR needed a web portal and mobile app to help interviewers fill out surveys as well as generate reports on the results.

It helps BSR's partners such as Nordstrom, Samsung, and Target work with factories, suppliers, and other workplaces in developing countries to evaluate the well-being of their workers, especially women.

Our solution kept BSR from having to manually manage survey responses and generate reports. This reduced errors, saved time, and made the processes more scalable.

How big?

BSR and its partners who represent some of the largest retail goods brands in the world measure work, health, financial, and self-esteem conditions in hundreds of workplaces using the portal and mobile app.

Our Role

Solspace created a Laravel framework powered Portal, where BSR administrators can manage partner accounts, enter survey data, and view reports based on those surveys all in an offline environment.

The Portal framework offers maximum flexibility on modifying data organization and concepts with time and has the ability to store data offline for later upload once internet access is available.

Helping understand Women's Health, Finances, and Self-Esteem in the Workplace

BSR runs HERproject, an endeavor to help better understand the health, financial, and self-esteem situation of Women in developing countries. BSR approached Solspace with the need to conduct surveys in areas of the world where even internet access may be scarce. Survey data needs to be gathered, stored, and processed to produce reports for BSR's partners in the project.

Data collection was to be conducted on either the portal or on tablets when interviewers were in the field. Most solutions assume an always-connected environment, so this was a unique requirement.

Laravel development

A close-to-the-metal, fully customized experience

Solspace needed a way to develop a portal and mobile app with maximum flexibility while avoiding having to fight software that uses its own nomenclature and development approach.

The Laravel framework was a perfect fit for this and enabled a portal developed with maximum flexibility while keeping the technical jargon to a minimum. Instead of discussing sections, channels, entries, custom fields, etc., BSR and Solspace could refer to BSR's terms: surveys, questions, answers, workplace programs, reports, etc.

Adapting to changing needs

With maximum flexibility and ample libraries and resources for Laravel, Solspace was able to provide for BSR's needs and adapt the portal to their changing methods.

Certain types of fields that weren't editable could be made editable. Relationships between concepts understood by BSR could be modified with ease. New data models and calculations could be created. APIs can be built. Export of large data sets could be constantly improved. All this is available while keeping the basic concepts in a language understood by BSR.

Api integrations

Store offline, offload later online

The mobile app had a particular requirement to be able to store survey data on the device while the device was offline, and allow to upload the stored data to the online portal once the BSR staff member returned to an environment with internet access.

Solspace made use of available storage methods available to web browsers, as well as a mobile development framework to detect the online/offline state of the application. We also created an Android app that can store dozens of survey entries while offline, and upload them to the portal later.


Ukrainian programmer received the award from Apple for the best game

Maxim Hryniv received the prestigious award for the Apple Mobile Games «iPad Game of the Year» for game «Contre Jour».

Maxim developed the game seven months and two more expected release of the game. The game was released August 24, 2011. Contre Jour was also the first game for iOS, has received a nomination for the E3 from GameCriticsAwards.

Previously, the game received a nomination and win in the category «iPad game of the Year» in Germany, Italy, the Netherlands, the United Kingdom, France, Spain, Japan and other countries.

In Contre Jour main character is a creature that passing the obstacles and collecting various items enhances their skills. The game contains 60 levels of varying difficulty.

Award iPad Game of the Year is considered one of the most prestigious handed her only once a year, and just one game.

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