25 research outputs found
Graph-based Patterns for Local Coherence Modeling
Coherence is an essential property of well-written texts. It distinguishes a multi-sentence text from a sequence of randomly strung sentences. The task of local coherence modeling is about the way that sentences in a text link up one another. Solving this task is beneficial for assessing the quality of texts. Moreover, a coherence model can be integrated into text generation systems such as text summarizers to produce coherent texts.
In this dissertation, we present a graph-based approach to local coherence modeling that accounts for the connectivity structure among sentences in a text. Graphs give our model the capability to take into account relations between non-adjacent sentences as well as those between adjacent sentences. Besides, the connectivity style among nodes in graphs reflects the relationships among sentences in a text.
We first employ the entity graph approach, proposed by Guinaudeau and Strube (2013), to represent a text via a graph. In the entity graph representation of a text, nodes encode sentences and edges depict the existence of a pair of coreferent mentions in sentences. We then devise graph-based features to capture the connectivity structure of nodes in a graph, and accordingly the connectivity structure of sentences in the corresponding text. We extract all subgraphs of entity graphs as features which encode the connectivity structure of graphs. Frequencies of subgraphs correlate with the perceived coherence of their corresponding texts. Therefore, we refer to these subgraphs as coherence patterns.
In order to complete our approach to coherence modeling, we propose a new graph representation of texts, rather than the entity graph. Our approach employs lexico-semantic relations among words in sentences, instead of only entity coreference relations, to model relationships between sentences via a graph. This new lexical graph representation of text plus our method for mining coherence patterns make our coherence model.
We evaluate our approach on the readability assessment task because a primary factor of readability is coherence. Coherent texts are easy to read and consequently demand less effort from their readers. Our extensive experiments on two separate readability assessment datasets show that frequencies of coherence patterns in texts correlate with the readability ratings assigned by human judges. By training a machine learning method on our coherence patterns, our model outperforms its counterparts on ranking texts with respect to their readability. As one of the ultimate goals of coherence models is to be used in text generation systems, we show how our coherence patterns can be integrated into a graph-based text summarizer to produce informative and coherent summaries. Our coherence patterns improve the performance of the summarization system based on both standard summarization metrics and human evaluations. An implementation of the approaches discussed in this dissertation is publicly available
Dialogue Coherence Assessment Without Explicit Dialogue Act Labels
Recent dialogue coherence models use the coherence features designed for
monologue texts, e.g. nominal entities, to represent utterances and then
explicitly augment them with dialogue-relevant features, e.g., dialogue act
labels. It indicates two drawbacks, (a) semantics of utterances is limited to
entity mentions, and (b) the performance of coherence models strongly relies on
the quality of the input dialogue act labels. We address these issues by
introducing a novel approach to dialogue coherence assessment. We use dialogue
act prediction as an auxiliary task in a multi-task learning scenario to obtain
informative utterance representations for coherence assessment. Our approach
alleviates the need for explicit dialogue act labels during evaluation. The
results of our experiments show that our model substantially (more than 20
accuracy points) outperforms its strong competitors on the DailyDialogue
corpus, and performs on par with them on the SwitchBoard corpus for ranking
dialogues concerning their coherence.Comment: Accepted at ACL 202
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Visual modifications to text are often used to obfuscate offensive comments
in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"),
among other scenarios. We consider this as a new type of adversarial attack in
NLP, a setting to which humans are very robust, as our experiments with both
simple and more difficult visual input perturbations demonstrate. We then
investigate the impact of visual adversarial attacks on current NLP systems on
character-, word-, and sentence-level tasks, showing that both neural and
non-neural models are, in contrast to humans, extremely sensitive to such
attacks, suffering performance decreases of up to 82\%. We then explore three
shielding methods---visual character embeddings, adversarial training, and
rule-based recovery---which substantially improve the robustness of the models.
However, the shielding methods still fall behind performances achieved in
non-attack scenarios, which demonstrates the difficulty of dealing with visual
attacks.Comment: Accepted as long paper at NAACL-2019; fixed one ungrammatical
sentenc
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
Document summarisation can be formulated as a sequential decision-making
problem, which can be solved by Reinforcement Learning (RL) algorithms. The
predominant RL paradigm for summarisation learns a cross-input policy, which
requires considerable time, data and parameter tuning due to the huge search
spaces and the delayed rewards. Learning input-specific RL policies is a more
efficient alternative but so far depends on handcrafted rewards, which are
difficult to design and yield poor performance. We propose RELIS, a novel RL
paradigm that learns a reward function with Learning-to-Rank (L2R) algorithms
at training time and uses this reward function to train an input-specific RL
policy at test time. We prove that RELIS guarantees to generate near-optimal
summaries with appropriate L2R and RL algorithms. Empirically, we evaluate our
approach on extractive multi-document summarisation. We show that RELIS reduces
the training time by two orders of magnitude compared to the state-of-the-art
models while performing on par with them.Comment: Accepted to IJCAI 201
Neural Network in Human Identification by DNA Sequences
In this paper we propose a new method to analyze the similarity/dissimilarity of DNA sequences which can be used in human identification field. This method is based on the graphical representation proposed by Randic et al. [1]. Instead of calculating the leading eigenvalues of the matrix for graphical representation we smooth the zigzag curve and calculate its curvature. Similarity between DNA sequences are decided by neural network. Our method is useful for human identification in criminal investigations and in Genetic disease. Our results verify the validity of our method