9 research outputs found
Cross-Language Question Re-Ranking
We study how to find relevant questions in community forums when the language
of the new questions is different from that of the existing questions in the
forum. In particular, we explore the Arabic-English language pair. We compare a
kernel-based system with a feed-forward neural network in a scenario where a
large parallel corpus is available for training a machine translation system,
bilingual dictionaries, and cross-language word embeddings. We observe that
both approaches degrade the performance of the system when working on the
translated text, especially the kernel-based system, which depends heavily on a
syntactic kernel. We address this issue using a cross-language tree kernel,
which compares the original Arabic tree to the English trees of the related
questions. We show that this kernel almost closes the performance gap with
respect to the monolingual system. On the neural network side, we use the
parallel corpus to train cross-language embeddings, which we then use to
represent the Arabic input and the English related questions in the same space.
The results also improve to close to those of the monolingual neural network.
Overall, the kernel system shows a better performance compared to the neural
network in all cases.Comment: SIGIR-2017; Community Question Answering; Cross-language Approaches;
Question Retrieval; Kernel-based Methods; Neural Networks; Distributed
Representation
Thread-level information for comment classification in community question answering
Community Question Answering (cQA) is a new application of QA in social contexts (e.g., fora). It presents new interesting challenges and research directions, e.g., exploiting the dependencies between the different comments of a thread to select the best answer for a given question. In this paper, we explored two ways of modeling such dependencies: (i) by designing specific features looking globally at the thread; and (ii) by applying structure prediction models. We trained and evaluated our models on data from SemEval-2015 Task 3 on Answer Selection in cQA. Our experiments show that: (i) the thread-level features consistently improve the performance for a variety of machine learning models, yielding state-of-the-art results; and (ii) sequential dependencies between the answer labels captured by structured prediction models are not enough to improve the results, indicating that more information is needed in the joint model
RankQA: Neural Question Answering with Answer Re-Ranking
The conventional paradigm in neural question answering (QA) for narrative
content is limited to a two-stage process: first, relevant text passages are
retrieved and, subsequently, a neural network for machine comprehension
extracts the likeliest answer. However, both stages are largely isolated in the
status quo and, hence, information from the two phases is never properly fused.
In contrast, this work proposes RankQA: RankQA extends the conventional
two-stage process in neural QA with a third stage that performs an additional
answer re-ranking. The re-ranking leverages different features that are
directly extracted from the QA pipeline, i.e., a combination of retrieval and
comprehension features. While our intentionally simple design allows for an
efficient, data-sparse estimation, it nevertheless outperforms more complex QA
systems by a significant margin: in fact, RankQA achieves state-of-the-art
performance on 3 out of 4 benchmark datasets. Furthermore, its performance is
especially superior in settings where the size of the corpus is dynamic. Here
the answer re-ranking provides an effective remedy against the underlying
noise-information trade-off due to a variable corpus size. As a consequence,
RankQA represents a novel, powerful, and thus challenging baseline for future
research in content-based QA.Comment: Accepted at ACL 2019; GitHub: https://github.com/bernhard2202/rankq
Multi-lingual Opinion Mining on YouTube
In order to successfully apply opinion mining (OM) to the large amounts of user-generated content produced every day, we need robust models that can handle the noisy input well yet can easily be adapted to a new domain or language. We here focus on opinion mining for YouTube by (i) modeling classifiers that predict the type of a comment and its polarity, while distinguishing whether the polarity is directed towards the product or video; (ii) proposing a robust shallow syntactic structure (STRUCT) that adapts well when tested across domains; and (iii) evaluating the effectiveness on the proposed structure on two languages, English and Italian. We rely on tree kernels to automatically extract and learn features with better generalization power than traditionally used bag-of-word models. Our extensive empirical evaluation shows that (i) STRUCT outperforms the bag-of-words model both within the same domain (up to 2.6% and 3% of absolute improvement for Italian and English, respectively); (ii) it is particularly useful when tested across domains (up to more than 4% absolute improvement for both languages), especially when little training data is available (up to 10% absolute improvement) and (iii) the proposed structure is also effective in a lower-resource language scenario, where only less accurate linguistic processing tools are available
Multitask Learning with Deep Neural Networks for Community Question Answering
In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question-comment similarity, question-question similarity and new question-comment similarity. The latter is the main task, which can exploit the previous two for achieving better results. Our DNN is trained jointly on all the three cQA tasks and learns to encode questions and comments into a single vector representation shared across the multiple tasks. The results on the official challenge test set show that our approach produces higher accuracy and faster convergence rates than the individual neural networks. Additionally, our method, which does not use any manual feature engineering, approaches the state of the art established with methods that make heavy use of it