Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach

Long-form question answering (LFQA) poses a challenge as it involves generating detailed answers in the form of paragraphs, which go beyond simple yes/no responses or short factual answers. While existing QA models excel in questions with concise answers, LFQA requires handling multiple topics and their intricate relationships, demanding comprehensive explanations. Previous attempts at LFQA focused on generating long-form answers by utilizing relevant contexts from a corpus, relying solely on the question itself. However, they overlooked the possibility that the question alone might not provide sufficient information to identify the relevant contexts. Additionally, generating detailed long-form answers often entails aggregating knowledge from diverse sources. To address these limitations, we propose an LFQA model with iterative Planning, Retrieval, and Generation. This iterative process continues until a complete answer is generated for the given question. From an extensive experiment on both an open domain and a technical domain QA dataset, we find that our model outperforms the state-of-the-art models on various textual and factual metrics for the LFQA task.

Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling

Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence information to be effective. However, in short texts, co-occurrence information is minimal, which results in feature sparsity in document representation. Therefore, existing topic models (probabilistic or neural) mostly fail to mine patterns from them to generate coherent topics. In this paper, we take a new approach to short-text topic modeling to address the data-sparsity issue by extending short text into longer sequences using existing pre-trained language models (PLMs). Besides, we provide a simple solution extending a neural topic model to reduce the effect of noisy out-of-topics text generation from PLMs. We observe that our model can substantially improve the performance of short-text topic modeling. Extensive experiments on multiple real-world datasets under extreme data sparsity scenarios show that our models can generate high-quality topics outperforming state-of-the-art models.

TopicAdapt- An Inter-Corpora Topics Adaptation Approach

Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivity to user guidance, sensitivity to the amount and quality of data, and the inability to adapt learned topics from one corpus to another. To address these challenges, this paper proposes a neural topic model, TopicAdapt, that can adapt relevant topics from a related source corpus and also discover new topics in a target corpus that are absent in the source corpus. The proposed model offers a promising approach to improve topic modeling performance in practical scenarios. Experiments over multiple datasets from diverse domains show the superiority of the proposed model against the state-of-the-art topic models.

Unsupervised Open-domain Keyphrase Generation

In this work, we study the problem of unsupervised open-domain keyphrase generation, where the objective is a keyphrase generation model that can be built without using human-labeled data and can perform consistently across domains. To solve this problem, we propose a seq2seq model that consists of two modules, namely \textit{phraseness} and \textit{informativeness} module, both of which can be built in an unsupervised and open-domain fashion. The phraseness module generates phrases, while the informativeness module guides the generation towards those that represent the core concepts of the text. We thoroughly evaluate our proposed method using eight benchmark datasets from different domains. Results on in-domain datasets show that our approach achieves state-of-the-art results compared with existing unsupervised models, and overall narrows the gap between supervised and unsupervised methods down to about 16\%. Furthermore, we demonstrate that our model performs consistently across domains, as it overall surpasses the baselines on out-of-domain datasets.

Coordinated Topic Modeling

We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes to model a corpus for easily understandable representation. This new task helps represent a corpus more interpretably by reusing existing knowledge and benefits the corpora comparison task. We design ECTM, an embedding-based coordinated topic model that effectively uses the reference representation to capture the target corpus-specific aspects while maintaining each topic's global semantics. In ECTM, we introduce the topic- and document-level supervision with a self-training mechanism to solve the problem. Finally, extensive experiments on multiple domains show the superiority of our model over other baselines.

Domain Representative Keywords Selection: A Probabilistic Approach

We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain. Such a task is crucial for many downstream tasks in natural language processing. To contrast the target domain and the context domain, we adapt the \textit{two-component mixture model} concept to generate a distribution of candidate keywords. It provides more importance to the \textit{distinctive} keywords of the target domain than common keywords contrasting with the context domain. To support the \textit{representativeness} of the selected keywords towards the target domain, we introduce an \textit{optimization algorithm} for selecting the subset from the generated candidate distribution. We have shown that the optimization algorithm can be efficiently implemented with a near-optimal approximation guarantee. Finally, extensive experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection.

Exploring Variational Graph Auto-Encoders for Extract Class Refactoring Recommendation

The code smell is a sign of design and development flaws in a software system that reduces the reusability and maintainability of the system. Refactoring is done as an ongoing practice to remove the code smell from the program code. Among different code smells, the God class or Blob is one of the most common code smells. A god class contains too many responsibilities, violating object-oriented programming design's low coupling and high cohesiveness principles. This paper proposes an automatic approach to extracting a God class into multiple smaller classes with more specific responsibilities. To do this, we first construct a graph of methods (as nodes) for the concerning god class. The edge between any two methods is determined by their structural similarity, and the feature for each method is initialized using different semantic representation methods. Then, the variational graph auto-encoder is used to learn a vector representation for each method. Finally, the learned vectors are used to cluster methods into different groups to be recommended as refactored classes. We assessed the proposed framework using three different class cohesion metrics on sixteen actual God Classes collected from two well-known open-source systems. We also conducted a comparative study of our approach with a similar existing approach and found that the proposed approach generated better results for almost all the God Classes used in the experiment.