Articles

Student course feedback is generated daily in both classrooms and online course discussion forums. Traditionally, instructors manually analyze these responses in a costly manner. In this work, we propose a new approach to sum- marizing student course feedback based on the integer linear programming (ILP) frame- work. Our approach allows …...

Community created content (e.g., product de- scriptions, reviews) typically discusses one entity at a time and it can be hard as well as time consuming for a user to compare two or more entities. In response, we de- fine a novel task of automatically generating entity comparisons from text. Our …...

A key challenge for timeline summarization is to generate a concise, yet complete storyline from large collections of news stories. Pre- vious studies in extractive timeline generation are limited in two ways: first, most prior work focuses on fully-observable ranking models or clustering models with hand-designed features that may not …...

Generation for Opinions and Arguments Lu Wang College of Computer and Information Science Northeastern University Boston, MA 02115 luwang@ccs.neu.edu Wang Ling Google DeepMind London, N1 0AE lingwang@google.com Abstract We study the problem of generating abstrac- tive summaries for opinionated text. We pro- pose an attention-based neural network model that is …...

Many languages use honorifics to express po- liteness, social distance, or the relative so- cial status between the speaker and their ad- dressee(s). In machine translation from a lan- guage without honorifics such as English, it is difficult to predict the appropriate honorific, but users may want to control the …...

The neural network joint model of transla- tion or NNJM (Devlin et al., 2014) combines source and target context to produce a power- ful translation feature. However, its softmax layer necessitates a sum over the entire output vocabulary, which results in very slow max- imum likelihood (MLE) training. This has …...

Active learning is a framework that makes it possible to efficiently train statistical models by selecting informative examples from a pool of unlabeled data. Previous work has found this framework effective for machine trans- lation (MT), making it possible to train bet- ter translation models with less effort, partic- ularly …...

We build a multi-source machine translation model and train it to maximize the probabil- ity of a target English string given French and German sources. Using the neural encoder- decoder framework, we explore several com- bination methods and report up to +4.8 Bleu increases on top of a very strong …...

A major benefit of tree-to-tree over tree- to-string translation is that we can use target-side syntax to improve reordering. While this is relatively simple for binarized constituency parses, the reordering prob- lem is considerably harder for dependency parses, in which words can have arbitrarily many children. Previous approaches have tackled …...

Automatic Machine Translation metrics, such as BLEU, are widely used in empirical eval- uation as a substitute for human assessment. Subsequently, the performance of a given met- ric is measured by its strength of correlation with human judgment. When a newly pro- posed metric achieves a stronger correlation over that …...

AbstractRecently, topic modeling has been widely applied in data mining due to its powerful ability. A common, major challenge in applying such topic models to other tasks is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, most …...

AbstractUsing neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based …...

AbstractVideo event detection is a challenging problem in information and multimedia retrieval. Different from single action detection, event detection requires a richer level of semantic information from video. In order to overcome this challenge, existing solutions often represent videos using high level features such as concepts. However, concept-based representation can …...

AbstractFor analyzing argumentative text, we propose to study the ‘depth’ of argumentation as one important component, which we distinguish from argument quality. In a pilot study with German newspaper commentary texts, we asked students to rate the degree of argumentativeness, and then looked for correlations with features of the annotated …...

AbstractTarget-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context …...

AbstractMachine learning-based methods have obtained great progress on emotion classification. However, in most previous studies, the models are learned based on a single corpus which often suffers from insufficient labeled data. In this paper, we propose a corpus fusion approach to address emotion classification across two corpora which use different …...

Character-based and word-based methods are two main types of statistical models for Chinese word segmentation, the for- mer exploiting sequence labeling models over characters and the latter typically ex- ploiting a transition-based model, with the advantages that word-level features can be easily utilized. Neural models have been exploited for character-based …...

Interpretability and discriminative power are the two most basic requirements for an evaluation metric. In this paper, we re- port the mention identification effect in the B3, CEAF, and BLANC coreference eval- uation metrics that makes it impossible to interpret their results properly. The only metric which is insensitive to …...

Most previous approaches to Chinese word segmentation formalize this prob- lem as a character-based sequence label- ing task so that only contextual informa- tion within fixed sized local windows and simple interactions between adjacent tags can be captured. In this paper, we pro- pose a novel neural framework which thor- …...

AbstractIn this paper we explore to what extent the difficulty of listening items in an English language proficiency test can be predicted by the textual properties of the prompt. We show that a system based on multiple text complexity features can predict item difficulty for several different item types and …...