Damianos Karakos


Cross-lingual Information Retrieval with BERT
Zhuolin Jiang | Amro El-Jaroudi | William Hartmann | Damianos Karakos | Lingjun Zhao
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents in the task of cross-lingual information retrieval. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision, using home-made CLIR training data derived from parallel corpora. Experimental results of the retrieval of Lithuanian documents against short English queries show that our model is effective and outperforms the competitive baseline approaches.

Reformulating Information Retrieval from Speech and Text as a Detection Problem
Damianos Karakos | Rabih Zbib | William Hartmann | Richard Schwartz | John Makhoul
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

In the IARPA MATERIAL program, information retrieval (IR) is treated as a hard detection problem; the system has to output a single global ranking over all queries, and apply a hard threshold on this global list to come up with all the hypothesized relevant documents. This means that how queries are ranked relative to each other can have a dramatic impact on performance. In this paper, we study such a performance measure, the Average Query Weighted Value (AQWV), which is a combination of miss and false alarm rates. AQWV requires that the same detection threshold is applied to all queries. Hence, detection scores of different queries should be comparable, and, to do that, a score normalization technique (commonly used in keyword spotting from speech) should be used. We describe unsupervised methods for score normalization, which are borrowed from the speech field and adapted accordingly for IR, and demonstrate that they greatly improve AQWV on the task of cross-language information retrieval (CLIR), on three low-resource languages used in MATERIAL. We also present a novel supervised score normalization approach which gives additional gains.

The 2019 BBN Cross-lingual Information Retrieval System
Le Zhang | Damianos Karakos | William Hartmann | Manaj Srivastava | Lee Tarlin | David Akodes | Sanjay Krishna Gouda | Numra Bathool | Lingjun Zhao | Zhuolin Jiang | Richard Schwartz | John Makhoul
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English. Foreign audio documents are first transcribed by a state-of-the-art pretrained multilingual speech recognition model that is finetuned to the target language. For text documents, we use multiple multilingual neural machine translation (MT) models to achieve good translation results, especially for low/medium resource languages. The processed documents and queries are then scored using a probabilistic CLIR model that makes use of the probability of translation from GIZA translation tables and scores from a Neural Network Lexical Translation Model (NNLTM). Additionally, advanced score normalization, combination, and thresholding schemes are employed to maximize the Average Query Weighted Value (AQWV) scores. The CLIR output, together with multiple translation renderings, are selected and translated into English snippets via a summarization model. Our turnkey system is language agnostic and can be quickly trained for a new low-resource language in few days.

What Set of Documents to Present to an Analyst?
Richard Schwartz | John Makhoul | Lee Tarlin | Damianos Karakos
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

We describe the human triage scenario envisioned in the Cross-Lingual Information Retrieval (CLIR) problem of the [REDUCT] Program. The overall goal is to maximize the quality of the set of documents that is given to a bilingual analyst, as measured by the AQWV score. The initial set of source documents that are retrieved by the CLIR system is summarized in English and presented to human judges who attempt to remove the irrelevant documents (false alarms); the resulting documents are then presented to the analyst. First, we describe the AQWV performance measure and show that, in our experience, if the acceptance threshold of the CLIR component has been optimized to maximize AQWV, the loss in AQWV due to false alarms is relatively constant across many conditions, which also limits the possible gain that can be achieved by any post filter (such as human judgments) that removes false alarms. Second, we analyze the likely benefits for the triage operation as a function of the initial CLIR AQWV score and the ability of the human judges to remove false alarms without removing relevant documents. Third, we demonstrate that we can increase the benefit for human judgments by combining the human judgment scores with the original document scores returned by the automatic CLIR system.


Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval
Lingjun Zhao | Rabih Zbib | Zhuolin Jiang | Damianos Karakos | Zhongqiang Huang
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages. Low resource languages often lack relevance annotations for CLIR, and when available the training data usually has limited coverage for possible queries. In this paper, we design a model which does not require relevance annotations, instead it is trained on samples extracted from translation corpora as weak supervision. This model relies on an attention mechanism to learn spans in the foreign sentence that are relevant to the query. We report experiments on two low resource languages: Swahili and Tagalog, trained on less that 100k parallel sentences each. The proposed model achieves 19 MAP points improvement compared to using CNNs for feature extraction, 12 points improvement from machine translation-based CLIR, and up to 6 points improvement compared to probabilistic CLIR models.


Morphological Segmentation for Keyword Spotting
Karthik Narasimhan | Damianos Karakos | Richard Schwartz | Stavros Tsakalidis | Regina Barzilay
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


Improved speech-to-text translation with the Fisher and Callhome Spanish-English speech translation corpus
Matt Post | Gaurav Kumar | Adam Lopez | Damianos Karakos | Chris Callison-Burch | Sanjeev Khudanpur
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon’s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.


Review of Hypothesis Alignment Algorithms for MT System Combination via Confusion Network Decoding
Antti-Veikko Rosti | Xiaodong He | Damianos Karakos | Gregor Leusch | Yuan Cao | Markus Freitag | Spyros Matsoukas | Hermann Ney | Jason Smith | Bing Zhang
Proceedings of the Seventh Workshop on Statistical Machine Translation


Description of the JHU System Combination Scheme for WMT 2011
Daguang Xu | Yuan Cao | Damianos Karakos
Proceedings of the Sixth Workshop on Statistical Machine Translation


Machine Translation System Combination using ITG-based Alignments
Damianos Karakos | Jason Eisner | Sanjeev Khudanpur | Markus Dreyer
Proceedings of ACL-08: HLT, Short Papers


Unigram Language Models using Diffusion Smoothing over Graphs
Bruno Jedynak | Damianos Karakos
Proceedings of the Second Workshop on TextGraphs: Graph-Based Algorithms for Natural Language Processing

Cross-Instance Tuning of Unsupervised Document Clustering Algorithms
Damianos Karakos | Jason Eisner | Sanjeev Khudanpur | Carey Priebe
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference


Generative Content Models for Structural Analysis of Medical Abstracts
Jimmy Lin | Damianos Karakos | Dina Demner-Fushman | Sanjeev Khudanpur
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology


Bootstrapping Without the Boot
Jason Eisner | Damianos Karakos
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing