pwc-archive/papers-with-abstracts huggingface dataset:
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
Authors: Antoine Tixier, Polykarpos Meladianos, Michalis Vazirgiannis
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Extractive Summarization, Extractive Text Summarization, Information Retrieval, Keyword Extraction, Sentence Compression, Text Summarization, Unsupervised Extractive Summarization

We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees. Our main contribution is the novel coverage reward term of the objective function optimized by the greedy algorithm. This component builds on the graph-of-words representation of text and the k-core decomposition algorithm to assign meaningful scores to words. We evaluate our approach on the AMI and ICSI meeting speech corpora, and on the DUC2001 news corpus. We reach state-of-the-art performance on all datasets. Results indicate that our method is particularly well-suited to the meeting domain.
TL;DR: Mining Reddit to Learn Automatic Summarization
Authors: Michael V{\"o}lske, Martin Potthast, Shahbaz Syed, Benno Stein
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Abstractive Text Summarization, Document Summarization, Text Summarization

Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.
Topic Model Stability for Hierarchical Summarization
Authors: John Miller, Kathleen Mccoy
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Articles, model, Text Summarization, Topic Models

We envisioned responsive generic hierarchical text summarization with summaries organized by section and paragraph based on hierarchical structure topic models. But we had to be sure that topic models were stable for the sampled corpora. To that end we developed a methodology for aligning multiple hierarchical structure topic models run over the same corpus under similar conditions, calculating a representative centroid model, and reporting stability of the centroid model. We ran stability experiments for standard corpora and a development corpus of Global Warming articles. We found flat and hierarchical structures of two levels plus the root offer stable centroid models, but hierarchical structures of three levels plus the root didn{'}t seem stable enough for use in hierarchical summarization.
Learning to Score System Summaries for Better Content Selection Evaluation.
Authors: Maxime Peyrard, Teresa Botschen, Iryna Gurevych
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Multi-Document Summarization, Semantic Textual Similarity

The evaluation of summaries is a challenging but crucial task of the summarization field. In this work, we propose to learn an automatic scoring metric based on the human judgements available as part of classical summarization datasets like TAC-2008 and TAC-2009. Any existing automatic scoring metrics can be included as features, the model learns the combination exhibiting the best correlation with human judgments. The reliability of the new metric is tested in a further manual evaluation where we ask humans to evaluate summaries covering the whole scoring spectrum of the metric. We release the trained metric as an open-source tool.
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization
Authors: Gholipour Ghal, Demian ari
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Document Summarization, Extractive Document Summarization, Extractive Text Summarization, Multi-Document Summarization

The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we apply this ranking to possible summaries instead of sentences and use a simple greedy algorithm to find the best summary. Furthermore, we show possibilities to scale up to larger input document collections by selecting a small number of sentences from each document prior to constructing the summary. Experiments were done on the DUC2004 dataset for multi-document summarization. We observe a higher performance over the original model, on par with more complex state-of-the-art methods.
Functions of Silences towards Information Flow in Spoken Conversation
Authors: Shammur Absar Chowdhury, Evgeny Stepanov, Morena Danieli, Giuseppe Riccardi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Clustering

Silence is an integral part of the most frequent turn-taking phenomena in spoken conversations. Silence is sized and placed within the conversation flow and it is coordinated by the speakers along with the other speech acts. The objective of this analytical study is twofold: to explore the functions of silence with duration of one second and above, towards information flow in a dyadic conversation utilizing the sequences of dialog acts present in the turns surrounding the silence itself; and to design a feature space useful for clustering the silences using a hierarchical concept formation algorithm. The resulting clusters are manually grouped into functional categories based on their similarities. It is observed that the silence plays an important role in response preparation, also can indicate speakers{'} hesitation or indecisiveness. It is also observed that sometimes long silences can be used deliberately to get a forced response from another speaker thus making silence a multi-functional and an important catalyst towards information flow.
Analyzing Human and Machine Performance In Resolving Ambiguous Spoken Sentences
Authors: Hussein Ghaly, M, Michael el
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentence

Written sentences can be more ambiguous than spoken sentences. We investigate this difference for two different types of ambiguity: prepositional phrase (PP) attachment and sentences where the addition of commas changes the meaning. We recorded a native English speaker saying several of each type of sentence both with and without disambiguating contextual information. These sentences were then presented either as text or audio and either with or without context to subjects who were asked to select the proper interpretation of the sentence. Results suggest that comma-ambiguous sentences are easier to disambiguate than PP-attachment-ambiguous sentences, possibly due to the presence of clear prosodic boundaries, namely silent pauses. Subject performance for sentences with PP-attachment ambiguity without context was 52{\%} for text only while it was 72.4{\%} for audio only, suggesting that audio has more disambiguating information than text. Using an analysis of acoustic features of two PP-attachment sentences, a simple classifier was implemented to resolve the PP-attachment ambiguity being early or late closure with a mean accuracy of 80{\%}.
Parsing transcripts of speech
Authors: Andrew Caines, Michael McCarthy, Paula Buttery
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

We present an analysis of parser performance on speech data, comparing word type and token frequency distributions with written data, and evaluating parse accuracy by length of input string. We find that parser performance tends to deteriorate with increasing length of string, more so for spoken than for written texts. We train an alternative parsing model with added speech data and demonstrate improvements in accuracy on speech-units, with no deterioration in performance on written text.
Enriching ASR Lattices with POS Tags for Dependency Parsing
Authors: Moritz Stiefel, Ngoc Thang Vu
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Dependency Parsing, POS, POS Tagging, speech-recognition, Speech Recognition, Spoken Language Understanding

Parsing speech requires a richer representation than 1-best or n-best hypotheses, e.g. lattices. Moreover, previous work shows that part-of-speech (POS) tags are a valuable resource for parsing. In this paper, we therefore explore a joint modeling approach of automatic speech recognition (ASR) and POS tagging to enrich ASR word lattices. To that end, we manipulate the ASR process from the pronouncing dictionary onward to use word-POS pairs instead of words. We evaluate ASR, POS tagging and dependency parsing (DP) performance demonstrating a successful lattice-based integration of ASR and POS tagging.
Spoken Term Discovery for Language Documentation using Translations
Authors: Antonios Anastasopoulos, Sameer Bansal, David Chiang, Sharon Goldwater, Adam Lopez
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Translation

Vast amounts of speech data collected for language documentation and research remain untranscribed and unsearchable, but often a small amount of speech may have text translations available. We present a method for partially labeling additional speech with translations in this scenario. We modify an unsupervised speech-to-translation alignment model and obtain prototype speech segments that match the translation words, which are in turn used to discover terms in the unlabelled data. We evaluate our method on a Spanish-English speech translation corpus and on two corpora of endangered languages, Arapaho and Ainu, demonstrating its appropriateness and applicability in an actual very-low-resource scenario.
Amharic-English Speech Translation in Tourism Domain
Authors: Michael Melese, Laurent Besacier, Million Meshesha
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation

This paper describes speech translation from Amharic-to-English, particularly Automatic Speech Recognition (ASR) with post-editing feature and Amharic-English Statistical Machine Translation (SMT). ASR experiment is conducted using morpheme language model (LM) and phoneme acoustic model(AM). Likewise,SMT conducted using word and morpheme as unit. Morpheme based translation shows a 6.29 BLEU score at a 76.4{\%} of recognition accuracy while word based translation shows a 12.83 BLEU score using 77.4{\%} word recognition accuracy. Further, after post-edit on Amharic ASR using corpus based n-gram, the word recognition accuracy increased by 1.42{\%}. Since post-edit approach reduces error propagation, the word based translation accuracy improved by 0.25 (1.95{\%}) BLEU score. We are now working towards further improving propagated errors through different algorithms at each unit of speech translation cascading component.
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks
Authors: Anastassia Loukina, Nitin Madnani, Aoife Cahill
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Automatic Speech Recognition (ASR), Speech Recognition

We consider the automatic scoring of a task for which both the content of the response as well its spoken fluency are important. We combine features from a text-only content scoring system originally designed for written responses with several categories of acoustic features. Although adding any single category of acoustic features to the text-only system on its own does not significantly improve performance, adding all acoustic features together does yield a small but significant improvement. These results are consistent for responses to open-ended questions and to questions focused on some given source material.
Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction
Authors: Sharid Lo{\'a}iciga, Sara Stymne, Preslav Nakov, Christian Hardmeier, J{\"o}rg Tiedemann, Mauro Cettolo, Yannick Versley
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document. We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that most participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.
Validation of an Automatic Metric for the Accuracy of Pronoun Translation (APT)
Authors: Lesly Miculicich Werlen, Andrei Popescu-Belis
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation

In this paper, we define and assess a reference-based metric to evaluate the accuracy of pronoun translation (APT). The metric automatically aligns a candidate and a reference translation using GIZA++ augmented with specific heuristics, and then counts the number of identical or different pronouns, with provision for legitimate variations and omitted pronouns. All counts are then combined into one score. The metric is applied to the results of seven systems (including the baseline) that participated in the DiscoMT 2015 shared task on pronoun translation from English to French. The APT metric reaches around 0.993-0.999 Pearson correlation with human judges (depending on the parameters of APT), while other automatic metrics such as BLEU, METEOR, or those specific to pronouns used at DiscoMT 2015 reach only 0.972-0.986 Pearson correlation.
Using a Graph-based Coherence Model in Document-Level Machine Translation
Authors: Leo Born, Mohsen Mesgar, Michael Strube
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Document Level Machine Translation, Machine Translation, Text Generation, Translation

Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube, (2016) with Docent (Hardmeier et al., 2012, Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.
Treatment of Markup in Statistical Machine Translation
Authors: Mathias M{\"u}ller
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Translation

We present work on handling XML markup in Statistical Machine Translation (SMT). The methods we propose can be used to effectively preserve markup (for instance inline formatting or structure) and to place markup correctly in a machine-translated segment. We evaluate our approaches with parallel data that naturally contains markup or where markup was inserted to create synthetic examples. In our experiments, hybrid reinsertion has proven the most accurate method to handle markup, while alignment masking and alignment reinsertion should be regarded as viable alternatives. We provide implementations of all the methods described and they are freely available as an open-source framework.
A BiLSTM-based System for Cross-lingual Pronoun Prediction
Authors: Sara Stymne, Sharid Lo{\'a}iciga, Fabienne Cap
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: General Classification, Machine Translation, Prediction, Sentence, Word Alignment

We describe the Uppsala system for the 2017 DiscoMT shared task on cross-lingual pronoun prediction. The system is based on a lower layer of BiLSTMs reading the source and target sentences respectively. Classification is based on the BiLSTM representation of the source and target positions for the pronouns. In addition we enrich our system with dependency representations from an external parser and character representations of the source sentence. We show that these additions perform well for German and Spanish as source languages. Our system is competitive and is in first or second place for all language pairs.
Neural Machine Translation for Cross-Lingual Pronoun Prediction
Authors: Sebastien Jean, Stanislas Lauly, Orhan Firat, Kyunghyun Cho
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, Prediction, Sentence, Translation

In this paper we present our systems for the DiscoMT 2017 cross-lingual pronoun prediction shared task. For all four language pairs, we trained a standard attention-based neural machine translation system as well as three variants that incorporate information from the preceding source sentence. We show that our systems, which are not specifically designed for pronoun prediction and may be used to generate complete sentence translations, generally achieve competitive results on this task.
Predicting Pronouns with a Convolutional Network and an N-gram Model
Authors: Christian Hardmeier
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, Word Embeddings

This paper describes the UU-Hardmeier system submitted to the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The system is an ensemble of convolutional neural networks combined with a source-aware n-gram language model.
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0
Authors: Juhani Luotolahti, Jenna Kanerva, Filip Ginter
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Word Embeddings

In this paper we present our system in the DiscoMT 2017 Shared Task on Crosslingual Pronoun Prediction. Our entry builds on our last year{'}s success, our system based on deep recurrent neural networks outperformed all the other systems with a clear margin. This year we investigate whether different pre-trained word embeddings can be used to improve the neural systems, and whether the recently published Gated Convolutions outperform the Gated Recurrent Units used last year.
Combining the output of two coreference resolution systems for two source languages to improve annotation projection
Authors: Yulia Grishina
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: coreference-resolution, Coreference Resolution, Machine Translation, Vocal Bursts Valence Prediction

Although parallel coreference corpora can to a high degree support the development of SMT systems, there are no large-scale parallel datasets available due to the complexity of the annotation task and the variability in annotation schemes. In this study, we exploit an annotation projection method to combine the output of two coreference resolution systems for two different source languages (English, German) in order to create an annotated corpus for a third language (Russian). We show that our technique is superior to projecting annotations from a single source language, and we provide an in-depth analysis of the projected annotations in order to assess the perspectives of our approach.
Discovery of Discourse-Related Language Contrasts through Alignment Discrepancies in English-German Translation
Authors: Ekaterina Lapshinova-Koltunski, Christian Hardmeier
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Sentence, Translation, Word Alignment

In this paper, we analyse alignment discrepancies for discourse structures in English-German parallel data {--} sentence pairs, in which discourse structures in target or source texts have no alignment in the corresponding parallel sentences. The discourse-related structures are designed in form of linguistic patterns based on the information delivered by automatic part-of-speech and dependency annotation. In addition to alignment errors (existing structures left unaligned), these alignment discrepancies can be caused by language contrasts or through the phenomena of explicitation and implicitation in the translation process. We propose a new approach including new type of resources for corpus-based language contrast analysis and apply it to study and classify the contrasts found in our English-German parallel corpus. As unaligned discourse structures may also result in the loss of discourse information in the MT training data, we hope to deliver information in support of discourse-aware machine translation (MT).
Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment
Authors: Hongzheng Li, Philippe Langlais, Yaohong Jin
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Implicit Relations, Machine Translation, Translation

Implicit discourse connectives and relations are distributed more widely in Chinese texts, when translating into English, such connectives are usually translated explicitly. Towards Chinese-English MT, in this paper we describe cross-lingual annotation and alignment of dis-course connectives in a parallel corpus, describing related surveys and findings. We then conduct some evaluation experiments to testify the translation of implicit connectives and whether representing implicit connectives explicitly in source language can improve the final translation performance significantly. Preliminary results show it has little improvement by just inserting explicit connectives for implicit relations.
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation
Authors: Laura Mascarell
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, Document Summarization, Information Retrieval, Machine Translation, Semantic Similarity, Semantic Textual Similarity, Translation, Word Embeddings

Currently under review for EMNLP 2017 The phrase-based Statistical Machine Translation (SMT) approach deals with sentences in isolation, making it difficult to consider discourse context in translation. This poses a challenge for ambiguous words that need discourse knowledge to be correctly translated. We propose a method that benefits from the semantic similarity in lexical chains to improve SMT output by integrating it in a document-level decoder. We focus on word embeddings to deal with the lexical chains, contrary to the traditional approach that uses lexical resources. Experimental results on German-to-English show that our method produces correct translations in up to 88{\%} of the changes, improving the translation in 36{\%}-48{\%} of them over the baseline.